{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\au206393\OneDrive - Aarhus universitet\Desktop\PSRM acceptance log-file\June 2025\Laustsen_et_al_PSRM_June2025.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}19 Jun 2025, 10:50:07
{txt}
{com}. 
. 
. *************************************************************** Import data-file *****************************************************************
. use "Laustsen_et_al_PSRM_June2025.dta", clear
{txt}
{com}. 
. **************************************************************************************************************************************************
. *************************************************************** RECODINGS ************************************************************************
. **************************************************************************************************************************************************
. 
. ***************************************************************** Wave 1 *************************************************************************
. 
. ************************************************** Demographic background variables **************************************************************
. * Sex
. recode w1_q3 (1=0 "Male") (2=1 "Female") (3=.), gen(sex)
{txt}(1,081 differences between {bf:w1_q3} and {bf:sex})

{com}. 
. * Age
. rename w1_q4 age
{res}{txt}
{com}. label variable age "Age"
{txt}
{com}. 
. * Education
. recode w1_q6 (1 2 =1 "Primary or High school") (3=2 "Professional-technical (vocational)") (4=3 "Incomplete higher") (5=4 "Bachelor degree") (6 7=5 "Master degree & Doctorate") (8=.), gen(education)
{txt}(1,048 differences between {bf:w1_q6} and {bf:education})

{com}. tab education

    {txt}RECODE of w1_q6 (6. What is the {c |}
highest level of education that you {c |}
                       have complet {c |}      Freq.     Percent        Cum.
{hline 36}{c +}{hline 35}
             Primary or High school {c |}{res}         95        8.86        8.86
{txt}Professional-technical (vocational) {c |}{res}        185       17.26       26.12
{txt}                  Incomplete higher {c |}{res}         85        7.93       34.05
{txt}                    Bachelor degree {c |}{res}        188       17.54       51.59
{txt}          Master degree & Doctorate {c |}{res}        519       48.41      100.00
{txt}{hline 36}{c +}{hline 35}
                              Total {c |}{res}      1,072      100.00
{txt}
{com}. 
. * Region
. clonevar region = w1_region_aggregate
{txt}
{com}. 
. 
. ************************************************ Experimental treatment for Ideal Leader Experiment **********************************************
. * Experimental treatment for leader trait evaluation questions
. recode w1_leader_exp_condition (1=1 "Conflict, now") (2=2 "Peace, future"), generate(Context)
{txt}(0 differences between {bf:w1_leader_exp_condition} and {bf:Context})

{com}. 
. clonevar Conflict_1 = Context
{txt}
{com}. 
. 
. *************************************** Leadership trait preferences in IDEAL LEADER *************************************************************
. * Competent
. recode w1_q14_1 (8=.)
{txt}(57 changes made to {bf:w1_q14_1})

{com}. generate Competence_1 = (w1_q14_1-1)/6
{txt}(57 missing values generated)

{com}. 
. * Trustworthy
. recode w1_q14_2 (8=.)
{txt}(36 changes made to {bf:w1_q14_2})

{com}. generate Trustworthy_1 = (w1_q14_2-1)/6
{txt}(36 missing values generated)

{com}. 
. * Dominant
. recode w1_q14_3 (8=.)
{txt}(44 changes made to {bf:w1_q14_3})

{com}. generate Dominant_1 = (w1_q14_3-1)/6
{txt}(44 missing values generated)

{com}. 
. * Generous
. recode w1_q14_4 (8=.)
{txt}(36 changes made to {bf:w1_q14_4})

{com}. generate Generous_1 = (w1_q14_4-1)/6
{txt}(36 missing values generated)

{com}. 
. * Strong
. recode w1_q14_5 (8=.)
{txt}(26 changes made to {bf:w1_q14_5})

{com}. generate Strong_1 = (w1_q14_5-1)/6
{txt}(26 missing values generated)

{com}. 
. * Warm
. recode w1_q14_6 (8=.)
{txt}(36 changes made to {bf:w1_q14_6})

{com}. generate Warm_1 = (w1_q14_6-1)/6
{txt}(36 missing values generated)

{com}. 
. * Tough-minded
. recode w1_q14_7 (8=.)
{txt}(39 changes made to {bf:w1_q14_7})

{com}. generate Toughminded_1 = (w1_q14_7-1)/6
{txt}(39 missing values generated)

{com}. 
. summ Competence_1 Trustworthy_1 Dominant_1 Generous_1 Strong_1 Warm_1 Toughminded_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Competence_1 {c |}{res}      1,024    .8802083    .1955989          0          1
{txt}Trustworth~1 {c |}{res}      1,045    .9197767    .1490432          0          1
{txt}{space 2}Dominant_1 {c |}{res}      1,037    .5940212    .2986719          0          1
{txt}{space 2}Generous_1 {c |}{res}      1,045    .7279107    .2446149          0          1
{txt}{space 4}Strong_1 {c |}{res}      1,055    .8840442    .1613094          0          1
{txt}{hline 13}{c +}{hline 57}
{space 6}Warm_1 {c |}{res}      1,045    .7090909    .2508124          0          1
{txt}Toughminde~1 {c |}{res}      1,042    .4328215    .2978518          0          1
{txt}
{com}. 
. 
. *** Exploring dimensions in trait impressions of IDEAL LEADER based on Principal Component Analysis
. factor Competence_1 Trustworthy_1 Strong_1 Warm_1 Generous_1 Dominant_1 Toughminded_1, pcf
{txt}(obs=988)

Factor analysis/correlation{col 50}Number of obs    = {res}       988
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      18

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.94691      1.63211            0.4210       0.4210
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      1.31480      0.15262            0.1878       0.6088
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      1.16218      0.64733            0.1660       0.7748
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.51485      0.07283            0.0736       0.8484
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.44202      0.11587            0.0631       0.9115
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}      0.32615      0.03306            0.0466       0.9581
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}      0.29309            .            0.0419       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}21{txt}) ={res} 2287.98{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:Competence_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7244}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1156}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4688}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2422}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7713}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0784}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4435}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2023}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7863}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0207}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2619}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3127}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6192}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3805}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5378}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1825}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6790}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3166}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5285}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1593}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5129}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6225}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3071}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2550}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3185}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8138}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1195}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2220}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. rotate, oblique oblimin

{txt}Factor analysis/correlation{col 50}Number of obs    = {res}       988
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: oblique oblimin (Kaiser off){col 50}Number of params =   {res}      18

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |}     Variance   Proportion    Rotated factors are correlated
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.52111       0.3602
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      2.07562       0.2965
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      1.65819       0.2369
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}21{txt}) ={res} 2287.98{txt} Prob>chi2 ={res} 0.0000

{txt}Rotated factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:Competence_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8948}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0361}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0660}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2422}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8997}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0118}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0110}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2023}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7419}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1038}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1418}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3127}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0077}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.9118}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0395}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1825}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0321}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8991}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0337}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1593}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0012}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1889}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8147}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2550}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0112}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1479}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8895}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2220}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

Factor rotation matrix

{space 4}{hline 13}{c  TT}{hline 9}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 7:Factor1}{space 1}{space 1}{ralign 7:Factor2}{space 1}{space 1}{ralign 7:Factor3}{space 1}
{space 4}{hline 13}{c   +}{hline 9}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:Factor1}{space 1}{c |}{space 1}{ralign 7:{res:{sf: 0.8723}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.7067}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.4645}}}{space 1}
{space 4}{space 0}{ralign 12:Factor2}{space 1}{c |}{space 1}{ralign 7:{res:{sf:-0.0817}}}{space 1}{space 1}{ralign 7:{res:{sf:-0.3812}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.8526}}}{space 1}
{space 4}{space 0}{ralign 12:Factor3}{space 1}{c |}{space 1}{ralign 7:{res:{sf:-0.4822}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.5961}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.2395}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 9}{hline 9}{hline 9}

{com}. 
. *** Produces Table SOM.1.a
. mat a = e(r_L)
{txt}
{com}. frmttable using TableSOM1a.rtf , statmat(a) sdec(2\2\2\2\2\2\2\0) replace  ctitles("Item","Component 1 (Competence)","Component 2 (Warmth)","Component 3 (Dominance)") rtitles(Competent\Trustworthy\Strong\Warm\Generous\Dominant\Toughminded\N)  title("Table SOM.1.a: Rotated factor loadings for trait ratings of ideal leader, survey Wave 1") note("N = 988")
{res}{txt:(note: file TableSOM1a.rtf not found)}
{txt}{center:Table SOM.1.a: Rotated factor loadings for trait ratings of ideal leader, survey Wave 1}
{txt}{center:{hline 88}}
{center:{txt}{lalign 13:Item}{txt}{center 26:Component 1 (Competence)}{txt}{center 22:Component 2 (Warmth)}{txt}{center 25:Component 3 (Dominance)}}
{txt}{center:{hline 88}}
{center:{txt}{lalign 13:Competent}{res}{center 26:0.89}{res}{center 22:-0.04}{res}{center 25:-0.07}}
{center:{txt}{lalign 13:Trustworthy}{res}{center 26:0.90}{res}{center 22:-0.01}{res}{center 25:-0.01}}
{center:{txt}{lalign 13:Strong}{res}{center 26:0.74}{res}{center 22:0.10}{res}{center 25:0.14}}
{center:{txt}{lalign 13:Warm}{res}{center 26:-0.01}{res}{center 22:0.91}{res}{center 25:-0.04}}
{center:{txt}{lalign 13:Generous}{res}{center 26:0.03}{res}{center 22:0.90}{res}{center 25:0.03}}
{center:{txt}{lalign 13:Dominant}{res}{center 26:0.00}{res}{center 22:0.19}{res}{center 25:0.81}}
{center:{txt}{lalign 13:Toughminded}{res}{center 26:0.01}{res}{center 22:-0.15}{res}{center 25:0.89}}
{center:{txt}{lalign 13:N}{res}{center 26:}{res}{center 22:}{res}{center 25:}}
{txt}{center:{hline 88}}
{txt}{center:N = 988}


{com}. 
. *** Generates factor score variables (for Wave 1) for robustness tests of main results
. factor Competence_1 Trustworthy_1 Strong_1 Warm_1 Generous_1 Dominant_1 Toughminded_1, pcf
{txt}(obs=988)

Factor analysis/correlation{col 50}Number of obs    = {res}       988
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      18

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.94691      1.63211            0.4210       0.4210
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      1.31480      0.15262            0.1878       0.6088
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      1.16218      0.64733            0.1660       0.7748
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.51485      0.07283            0.0736       0.8484
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.44202      0.11587            0.0631       0.9115
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}      0.32615      0.03306            0.0466       0.9581
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}      0.29309            .            0.0419       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}21{txt}) ={res} 2287.98{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:Competence_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7244}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1156}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4688}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2422}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7713}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0784}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4435}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2023}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7863}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0207}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2619}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3127}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6192}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3805}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5378}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1825}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6790}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3166}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5285}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1593}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5129}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6225}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3071}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2550}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3185}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8138}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1195}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2220}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. rotate, oblique oblimin

{txt}Factor analysis/correlation{col 50}Number of obs    = {res}       988
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: oblique oblimin (Kaiser off){col 50}Number of params =   {res}      18

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |}     Variance   Proportion    Rotated factors are correlated
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.52111       0.3602
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      2.07562       0.2965
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      1.65819       0.2369
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}21{txt}) ={res} 2287.98{txt} Prob>chi2 ={res} 0.0000

{txt}Rotated factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:Competence_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8948}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0361}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0660}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2422}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8997}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0118}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0110}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2023}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7419}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1038}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1418}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3127}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0077}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.9118}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0395}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1825}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0321}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8991}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0337}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1593}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0012}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1889}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8147}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2550}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0112}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1479}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8895}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2220}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

Factor rotation matrix

{space 4}{hline 13}{c  TT}{hline 9}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 7:Factor1}{space 1}{space 1}{ralign 7:Factor2}{space 1}{space 1}{ralign 7:Factor3}{space 1}
{space 4}{hline 13}{c   +}{hline 9}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:Factor1}{space 1}{c |}{space 1}{ralign 7:{res:{sf: 0.8723}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.7067}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.4645}}}{space 1}
{space 4}{space 0}{ralign 12:Factor2}{space 1}{c |}{space 1}{ralign 7:{res:{sf:-0.0817}}}{space 1}{space 1}{ralign 7:{res:{sf:-0.3812}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.8526}}}{space 1}
{space 4}{space 0}{ralign 12:Factor3}{space 1}{c |}{space 1}{ralign 7:{res:{sf:-0.4822}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.5961}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.2395}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 9}{hline 9}{hline 9}

{com}. predict Comp_PCA_1 Warm_PCA_1 Domi_PCA_1
{txt}(option {bf:regression} assumed; regression scoring)

{p 0 0 2}Scoring coefficients (method = regression; based on oblimin(0) rotated factors){p_end}

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:Competence_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.41606}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.03323}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.05736}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.41715}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.01981}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.02064}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.34010}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.04823}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.08340}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.01619}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.53466}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.03833}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.00139}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.52569}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.01066}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.01427}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.10008}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.54781}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.00586}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.09827}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.60254}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}


{com}. corr Comp_PCA_1 Warm_PCA_1 Domi_PCA_1
{txt}(obs=988)

             {c |} Comp_P~1 Warm_P~1 Domi_P~1
{hline 13}{c +}{hline 27}
  Comp_PCA_1 {c |}{res}   1.0000
  {txt}Warm_PCA_1 {c |}{res}   0.3601   1.0000
  {txt}Domi_PCA_1 {c |}{res}   0.2201   0.1460   1.0000

{txt}
{com}. 
. *** Main outcome variables for Wabe 1: Composite scales for dominance, warmth and competence (on 0-1 scales)
. egen Domi_scale_1 = rowmean(Dominant_1 Toughminded_1)
{txt}(30 missing values generated)

{com}. 
. egen Comp_scale_1 = rowmean(Competence_1 Trustworthy_1 Strong_1)
{txt}(24 missing values generated)

{com}. 
. egen Warm_scale_1 = rowmean(Warm_1 Generous_1)
{txt}(26 missing values generated)

{com}. 
. summ Domi_scale_1 Comp_scale_1 Warm_scale_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Domi_scale_1 {c |}{res}      1,051     .512052    .2588131          0          1
{txt}Comp_scale_1 {c |}{res}      1,057    .8936718     .145519          0          1
{txt}Warm_scale_1 {c |}{res}      1,055    .7185624    .2263167          0          1
{txt}
{com}. corr Domi_scale_1 Comp_scale_1 Warm_scale_1
{txt}(obs=1,044)

             {c |} Domi_s~1 Comp_s~1 Warm_s~1
{hline 13}{c +}{hline 27}
Domi_scale_1 {c |}{res}   1.0000
{txt}Comp_scale_1 {c |}{res}   0.2295   1.0000
{txt}Warm_scale_1 {c |}{res}   0.1570   0.3740   1.0000

{txt}
{com}. 
. 
. ****************************************** Leadership trait perceptions of CURRENT LEADER, Zelenskyy *********************************************
. * Competent
. recode w1_q15_1 (8=.)
{txt}(23 changes made to {bf:w1_q15_1})

{com}. generate Zel_Comp_1 = (w1_q15_1-1)/6
{txt}(23 missing values generated)

{com}. 
. * Trustworthy
. recode w1_q15_2 (8=.)
{txt}(23 changes made to {bf:w1_q15_2})

{com}. generate Zel_Trust_1 = (w1_q15_2-1)/6
{txt}(23 missing values generated)

{com}. 
. * Dominant
. recode w1_q15_3 (8=.)
{txt}(42 changes made to {bf:w1_q15_3})

{com}. generate Zel_Domi_1 = (w1_q15_3-1)/6
{txt}(42 missing values generated)

{com}. 
. * Generous
. recode w1_q15_4 (8=.)
{txt}(38 changes made to {bf:w1_q15_4})

{com}. generate Zel_Generous_1 = (w1_q15_4-1)/6
{txt}(38 missing values generated)

{com}. 
. * Strong
. recode w1_q15_5 (8=.)
{txt}(22 changes made to {bf:w1_q15_5})

{com}. generate Zel_Strong_1 = (w1_q15_5-1)/6
{txt}(22 missing values generated)

{com}. 
. * Warm
. recode w1_q15_6 (8=.)
{txt}(34 changes made to {bf:w1_q15_6})

{com}. generate Zel_Warm_1 = (w1_q15_6-1)/6
{txt}(34 missing values generated)

{com}. 
. * Tough-minded
. recode w1_q15_7 (8=.)
{txt}(43 changes made to {bf:w1_q15_7})

{com}. generate Zel_Tough_1 = (w1_q15_7-1)/6
{txt}(43 missing values generated)

{com}. 
. 
. summ Zel_Comp_1 Zel_Trust_1 Zel_Domi_1 Zel_Generous_1 Zel_Strong_1 Zel_Warm_1 Zel_Tough_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}Zel_Comp_1 {c |}{res}      1,058    .7463768    .3055257          0          1
{txt}{space 1}Zel_Trust_1 {c |}{res}      1,058    .7840265    .2991983          0          1
{txt}{space 2}Zel_Domi_1 {c |}{res}      1,039     .563683    .3329842          0          1
{txt}Zel_Genero~1 {c |}{res}      1,043    .7102908    .3076971          0          1
{txt}Zel_Strong_1 {c |}{res}      1,059    .7610954    .3130257          0          1
{txt}{hline 13}{c +}{hline 57}
{space 2}Zel_Warm_1 {c |}{res}      1,047    .7184018    .3060069          0          1
{txt}{space 1}Zel_Tough_1 {c |}{res}      1,038    .3908157    .3077853          0          1
{txt}
{com}. 
. 
. ** Creates composite scales for perceptions of Zelenskyy on the same three trait dimensions as for "ideal leader ratings": dominance, warmth and competence.
. egen Comp_scale_Zel1 = rowmean(Zel_Comp_1 Zel_Trust_1 Zel_Strong_1)
{txt}(18 missing values generated)

{com}. alpha Zel_Comp_1 Zel_Trust_1 Zel_Strong_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0798795
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.9458
{txt}
{com}. 
. egen Warm_scale_Zel1 = rowmean(Zel_Warm_1 Zel_Generous_1)
{txt}(27 missing values generated)

{com}. alpha Zel_Warm_1 Zel_Generous_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0786946
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.9105
{txt}
{com}. 
. egen Domi_scale_Zel1= rowmean(Zel_Domi_1 Zel_Tough_1)
{txt}(29 missing values generated)

{com}. alpha Zel_Domi_1 Zel_Tough_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  .050889
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.6622
{txt}
{com}. 
. summ Comp_scale_Zel1 Warm_scale_Zel1 Domi_scale_Zel1 

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Comp_scal~l1 {c |}{res}      1,063    .7637713    .2908337          0          1
{txt}Warm_scal~l1 {c |}{res}      1,054    .7145003    .2931831          0          1
{txt}Domi_scal~l1 {c |}{res}      1,052    .4771863    .2808222          0          1
{txt}
{com}. 
. 
. 
. 
. 
. ****************************************************** Self-reported emotional reactions over last week ******************************************
. * Afraid
. recode w1_q11_1 (8=.), generate(afraid_1)
{txt}(18 differences between {bf:w1_q11_1} and {bf:afraid_1})

{com}. * Frightened
. recode w1_q11_2 (8=.), generate(frightened_1)
{txt}(14 differences between {bf:w1_q11_2} and {bf:frightened_1})

{com}. * Scared
. recode w1_q11_3 (8=.), generate(scared_1)
{txt}(20 differences between {bf:w1_q11_3} and {bf:scared_1})

{com}. 
. ** Composite scale for anxiety
. corr afraid_1 frightened_1 scared_1
{txt}(obs=1,056)

             {c |} afraid_1 fright~1 scared_1
{hline 13}{c +}{hline 27}
    afraid_1 {c |}{res}   1.0000
{txt}frightened_1 {c |}{res}   0.8053   1.0000
    {txt}scared_1 {c |}{res}   0.7059   0.7202   1.0000

{txt}
{com}. alpha afraid_1 frightened_1 scared_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  2.42818
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8956
{txt}
{com}. egen fearfull_scale_W1_7 = rowmean(afraid_1 frightened_1 scared_1)
{txt}(11 missing values generated)

{com}. generate fearfull_scale_1 = (fearfull_scale_W1_7-1)/6
{txt}(11 missing values generated)

{com}. 
. * Angry
. recode w1_q11_4 (8=.), generate(angry_1)
{txt}(17 differences between {bf:w1_q11_4} and {bf:angry_1})

{com}. * Hostile
. recode w1_q11_5 (8=.), generate(hostile_1)
{txt}(37 differences between {bf:w1_q11_5} and {bf:hostile_1})

{com}. * Disgusted
. recode w1_q11_6 (8=.), generate(disgusted_1)
{txt}(37 differences between {bf:w1_q11_6} and {bf:disgusted_1})

{com}. 
. ** Composite scale for agressive emotions
. corr angry_1 hostile_1 disgusted_1
{txt}(obs=1,021)

             {c |}  angry_1 hostil~1 disgus~1
{hline 13}{c +}{hline 27}
     angry_1 {c |}{res}   1.0000
   {txt}hostile_1 {c |}{res}   0.5608   1.0000
 {txt}disgusted_1 {c |}{res}   0.5519   0.4918   1.0000

{txt}
{com}. alpha angry_1 hostile_1 disgusted_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.540405
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7685
{txt}
{com}. egen aggressive_scale_W1_7 = rowmean(angry_1 hostile_1 disgusted_1)
{txt}(11 missing values generated)

{com}. generate aggressive_scale_1 = (aggressive_scale_W1_7-1)/6
{txt}(11 missing values generated)

{com}. 
. * Sad
. recode w1_q11_7 (8=.), generate(sad_1)
{txt}(11 differences between {bf:w1_q11_7} and {bf:sad_1})

{com}. * Lonely
. recode w1_q11_8 (8=.), generate(lonely_1)
{txt}(23 differences between {bf:w1_q11_8} and {bf:lonely_1})

{com}. * Downhearted
. recode w1_q11_9 (8=.), generate(downhearted_1)
{txt}(16 differences between {bf:w1_q11_9} and {bf:downhearted_1})

{com}. 
. ** Composite scale for sadness
. corr sad_1 lonely_1 downhearted_1 
{txt}(obs=1,054)

             {c |}    sad_1 lonely_1 downhe~1
{hline 13}{c +}{hline 27}
       sad_1 {c |}{res}   1.0000
    {txt}lonely_1 {c |}{res}   0.3668   1.0000
{txt}downhearte~1 {c |}{res}   0.6018   0.4300   1.0000

{txt}
{com}. alpha sad_1 lonely_1 downhearted_1 

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.410318
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7230
{txt}
{com}. egen sadness_scale_W1_7 = rowmean(sad_1 lonely_1 downhearted_1)
{txt}(7 missing values generated)

{com}. generate sadness_scale_1 = (sadness_scale_W1_7-1)/6
{txt}(7 missing values generated)

{com}. 
. * Proud
. recode w1_q11_10 (8=.), generate(proud_1)
{txt}(26 differences between {bf:w1_q11_10} and {bf:proud_1})

{com}. * Strong
. recode w1_q11_11 (8=.), generate(strong_1)
{txt}(23 differences between {bf:w1_q11_11} and {bf:strong_1})

{com}. * Confident
. recode w1_q11_12 (8=.), generate(confident_1)
{txt}(18 differences between {bf:w1_q11_12} and {bf:confident_1})

{com}. 
. ** Composite scale for self-confident emotions
. corr proud_1 strong_1 confident_1
{txt}(obs=1,041)

             {c |}  proud_1 strong_1 confid~1
{hline 13}{c +}{hline 27}
     proud_1 {c |}{res}   1.0000
    {txt}strong_1 {c |}{res}   0.5553   1.0000
 {txt}confident_1 {c |}{res}   0.4869   0.6624   1.0000

{txt}
{com}. alpha proud_1 strong_1 confident_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.484637
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7922
{txt}
{com}. egen selfconf_scale_W1_7 = rowmean(proud_1 strong_1 confident_1)
{txt}(11 missing values generated)

{com}. generate selfconf_scale_1 = (selfconf_scale_W1_7-1)/6
{txt}(11 missing values generated)

{com}. 
. summ fearfull_scale_1 aggressive_scale_1 sadness_scale_1 selfconf_scale_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
fearfull_s~1 {c |}{res}      1,070    .4757529    .2744772          0          1
{txt}aggressive~1 {c |}{res}      1,070    .6362669    .2370329          0          1
{txt}sadness_sc~1 {c |}{res}      1,074    .5127767    .2336689          0          1
{txt}selfconf_s~1 {c |}{res}      1,070    .6069574    .2299484          0          1
{txt}
{com}. 
. 
. 
. 
. 
. ******************************************** Self-reported Victimization of Russian Attacks ******************************************************
. **** Inspects all three items
. codebook w1_q9_1 w1_q9_2 w1_q9_3

{txt}{hline}
{res}w1_q9_1{right:9.1 How often: The invading Russian or pro-Russian forces have directly attacked}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:labels11_wave1}

{col 18}Range: [{res}1{txt},{res}6{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}6{col 51}{txt}Missing .: {res}0{txt}/{res}1,081

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}       803{col 32}       1{col 42}{txt}Never
{col 20}{res}        81{col 32}       2{col 42}{txt}Once
{col 20}{res}        57{col 32}       3{col 42}{txt}2 to 4 times
{col 20}{res}        28{col 32}       4{col 42}{txt}5 to 10 times
{col 20}{res}        44{col 32}       5{col 42}{txt}More than 10 times
{col 20}{res}        68{col 32}       6{col 42}{txt}Prefer not to say

{txt}{hline}
{res}w1_q9_2{right:9.2 How often: The invading Russian or pro-Russian forces have directly attacked}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:labels11_wave1}

{col 18}Range: [{res}1{txt},{res}6{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}6{col 51}{txt}Missing .: {res}0{txt}/{res}1,081

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}       646{col 32}       1{col 42}{txt}Never
{col 20}{res}       100{col 32}       2{col 42}{txt}Once
{col 20}{res}        96{col 32}       3{col 42}{txt}2 to 4 times
{col 20}{res}        75{col 32}       4{col 42}{txt}5 to 10 times
{col 20}{res}       105{col 32}       5{col 42}{txt}More than 10 times
{col 20}{res}        59{col 32}       6{col 42}{txt}Prefer not to say

{txt}{hline}
{res}w1_q9_3{right:9.3 How often: The invading Russian or pro-Russian forces have directly attacked}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:labels11_wave1}

{col 18}Range: [{res}1{txt},{res}6{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}6{col 51}{txt}Missing .: {res}0{txt}/{res}1,081

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}       417{col 32}       1{col 42}{txt}Never
{col 20}{res}       136{col 32}       2{col 42}{txt}Once
{col 20}{res}       169{col 32}       3{col 42}{txt}2 to 4 times
{col 20}{res}       107{col 32}       4{col 42}{txt}5 to 10 times
{col 20}{res}       197{col 32}       5{col 42}{txt}More than 10 times
{col 20}{res}        55{col 32}       6{col 42}{txt}Prefer not to say

{com}. tab1 w1_q9_1 w1_q9_2 w1_q9_3

{res}-> tabulation of w1_q9_1  

{txt}9.1 How often: The {c |}
  invading Russian {c |}
    or pro-Russian {c |}
       forces have {c |}
 directly attacked {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
             Never {c |}{res}        803       74.28       74.28
{txt}              Once {c |}{res}         81        7.49       81.78
{txt}      2 to 4 times {c |}{res}         57        5.27       87.05
{txt}     5 to 10 times {c |}{res}         28        2.59       89.64
{txt}More than 10 times {c |}{res}         44        4.07       93.71
{txt} Prefer not to say {c |}{res}         68        6.29      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}      1,081      100.00

-> tabulation of w1_q9_2  

{txt}9.2 How often: The {c |}
  invading Russian {c |}
    or pro-Russian {c |}
       forces have {c |}
 directly attacked {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
             Never {c |}{res}        646       59.76       59.76
{txt}              Once {c |}{res}        100        9.25       69.01
{txt}      2 to 4 times {c |}{res}         96        8.88       77.89
{txt}     5 to 10 times {c |}{res}         75        6.94       84.83
{txt}More than 10 times {c |}{res}        105        9.71       94.54
{txt} Prefer not to say {c |}{res}         59        5.46      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}      1,081      100.00

-> tabulation of w1_q9_3  

{txt}9.3 How often: The {c |}
  invading Russian {c |}
    or pro-Russian {c |}
       forces have {c |}
 directly attacked {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
             Never {c |}{res}        417       38.58       38.58
{txt}              Once {c |}{res}        136       12.58       51.16
{txt}      2 to 4 times {c |}{res}        169       15.63       66.79
{txt}     5 to 10 times {c |}{res}        107        9.90       76.69
{txt}More than 10 times {c |}{res}        197       18.22       94.91
{txt} Prefer not to say {c |}{res}         55        5.09      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}      1,081      100.00
{txt}
{com}. recode w1_q9_1 w1_q9_2 w1_q9_3 (6=.)
{txt}(68 changes made to {bf:w1_q9_1})
(59 changes made to {bf:w1_q9_2})
(55 changes made to {bf:w1_q9_3})

{com}. rename w1_q9_1 w1_victim_self
{res}{txt}
{com}. rename w1_q9_2 w1_victim_family
{res}{txt}
{com}. rename w1_q9_3 w1_victim_other
{res}{txt}
{com}. corr w1_victim_self w1_victim_family w1_victim_other
{txt}(obs=993)

             {c |} w1_vic~f w1_vic~y w1_vic~r
{hline 13}{c +}{hline 27}
w1_victim_~f {c |}{res}   1.0000
{txt}w1_victim_~y {c |}{res}   0.4590   1.0000
{txt}w1_victim_~r {c |}{res}   0.3194   0.6462   1.0000

{txt}
{com}. alpha w1_victim_self w1_victim_family w1_victim_other

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .8512443
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7277
{txt}
{com}. 
. ** Generates victimization scale
. egen Victimization_W1_5 = rowmean(w1_victim_self w1_victim_family w1_victim_other)
{txt}(38 missing values generated)

{com}. generate Victimization_1 = (Victimization_W1_5-1)/4
{txt}(38 missing values generated)

{com}. summ Victimization_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Victimizat~1 {c |}{res}      1,043    .2472435    .2787995          0          1
{txt}
{com}. 
. 
. 
. ******************************************** Identification with Ukraine, Russia and Europe ******************************************************
. ** Ukraine
. recode w1_q23_1 (8=.), generate(Ukraine_ID_W1_7)
{txt}(29 differences between {bf:w1_q23_1} and {bf:Ukraine_ID_W1_7})

{com}. recode w1_q24_1 (8=.), generate(Ukraine_close_W1_7)
{txt}(24 differences between {bf:w1_q24_1} and {bf:Ukraine_close_W1_7})

{com}. corr Ukraine_ID_W1_7 Ukraine_close_W1_7
{txt}(obs=1,045)

             {c |} U~D_W1_7 U~e_W1_7
{hline 13}{c +}{hline 18}
Ukraine_ID~7 {c |}{res}   1.0000
{txt}Ukraine_cl~7 {c |}{res}   0.7665   1.0000

{txt}
{com}. egen ID_Ukraine_W1_7 = rowmean(Ukraine_ID_W1_7 Ukraine_close_W1_7)
{txt}(17 missing values generated)

{com}. generate ID_Ukraine_1 = (ID_Ukraine_W1_7-1)/6
{txt}(17 missing values generated)

{com}. 
. ** Russia
. recode w1_q23_2 (8=.), generate(Russia_ID_W1_7)
{txt}(48 differences between {bf:w1_q23_2} and {bf:Russia_ID_W1_7})

{com}. recode w1_q24_2 (8=.), generate(Russia_close_W1_7)
{txt}(37 differences between {bf:w1_q24_2} and {bf:Russia_close_W1_7})

{com}. corr Russia_ID_W1_7 Russia_close_W1_7
{txt}(obs=1,025)

             {c |} R~D_W1_7 R~e_W1_7
{hline 13}{c +}{hline 18}
Russia_ID_~7 {c |}{res}   1.0000
{txt}Russia_clo~7 {c |}{res}   0.7635   1.0000

{txt}
{com}. egen ID_Russia_W1_7 = rowmean(Russia_ID_W1_7 Russia_close_W1_7)
{txt}(29 missing values generated)

{com}. generate ID_Russia_1 = (ID_Russia_W1_7-1)/6
{txt}(29 missing values generated)

{com}. 
. ** Europe
. recode w1_q23_3 (8=.), generate(Europe_ID_W1_7)
{txt}(40 differences between {bf:w1_q23_3} and {bf:Europe_ID_W1_7})

{com}. recode w1_q24_3 (8=.), generate(Europe_close_W1_7)
{txt}(27 differences between {bf:w1_q24_3} and {bf:Europe_close_W1_7})

{com}. corr Europe_ID_W1_7 Europe_close_W1_7
{txt}(obs=1,037)

             {c |} E~D_W1_7 E~e_W1_7
{hline 13}{c +}{hline 18}
Europe_ID_~7 {c |}{res}   1.0000
{txt}Europe_clo~7 {c |}{res}   0.7978   1.0000

{txt}
{com}. egen ID_Europe_W1_7 = rowmean(Europe_ID_W1_7 Europe_close_W1_7)
{txt}(23 missing values generated)

{com}. generate ID_Europe_1 = (ID_Europe_W1_7-1)/6
{txt}(23 missing values generated)

{com}. 
. summ ID_Ukraine_1 ID_Russia_1 ID_Europe_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
ID_Ukraine_1 {c |}{res}      1,064    .9348371    .1526613          0          1
{txt}{space 1}ID_Russia_1 {c |}{res}      1,052    .1351394    .2279704          0          1
{txt}{space 1}ID_Europe_1 {c |}{res}      1,058    .7063642    .2693415          0          1
{txt}
{com}. 
. 
. 
. 
. **************************************************************************************************************************************************
. *************************************************************** WAVE 2 ***************************************************************************
. **************************************************************************************************************************************************
. 
. ****************************************** Leadership trait preferences in IDEAL LEADER **********************************************************
. * Competent
. recode w2_q12_1 (8=.)
{txt}(28 changes made to {bf:w2_q12_1})

{com}. generate Competence_2 = (w2_q12_1-1)/6
{txt}(298 missing values generated)

{com}. 
. * Trustworthy
. recode w2_q12_2 (8=.)
{txt}(31 changes made to {bf:w2_q12_2})

{com}. generate Trustworthy_2 = (w2_q12_2-1)/6
{txt}(301 missing values generated)

{com}. 
. * Dominant
. recode w2_q12_3 (8=.)
{txt}(50 changes made to {bf:w2_q12_3})

{com}. generate Dominant_2 = (w2_q12_3-1)/6
{txt}(320 missing values generated)

{com}. 
. * Generous
. recode w2_q12_4 (8=.)
{txt}(38 changes made to {bf:w2_q12_4})

{com}. generate Generous_2 = (w2_q12_4-1)/6
{txt}(308 missing values generated)

{com}. 
. * Strong
. recode w2_q12_5 (8=.)
{txt}(27 changes made to {bf:w2_q12_5})

{com}. generate Strong_2 = (w2_q12_5-1)/6
{txt}(297 missing values generated)

{com}. 
. * Warm
. recode w2_q12_6 (8=.)
{txt}(45 changes made to {bf:w2_q12_6})

{com}. generate Warm_2 = (w2_q12_6-1)/6
{txt}(315 missing values generated)

{com}. 
. * Tough-minded
. recode w2_q12_7 (8=.)
{txt}(45 changes made to {bf:w2_q12_7})

{com}. generate Toughminded_2 = (w2_q12_7-1)/6
{txt}(315 missing values generated)

{com}. 
. summ Competence_2 Trustworthy_2 Dominant_2 Generous_2 Strong_2 Warm_2 Toughminded_2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Competence_2 {c |}{res}        783    .8997446    .1773713          0          1
{txt}Trustworth~2 {c |}{res}        780    .9213675    .1557599          0          1
{txt}{space 2}Dominant_2 {c |}{res}        761    .5516864    .2980696          0          1
{txt}{space 2}Generous_2 {c |}{res}        773    .6836999    .2757289          0          1
{txt}{space 4}Strong_2 {c |}{res}        784    .8988095    .1647398          0          1
{txt}{hline 13}{c +}{hline 57}
{space 6}Warm_2 {c |}{res}        766    .6668842    .2672514          0          1
{txt}Toughminde~2 {c |}{res}        766    .5047868    .2929434          0          1
{txt}
{com}. 
. 
. *** Exploring dimensions in trait impressions of IDEAL LEADER based on Principal Component Analysis in Wave 2
. factor Competence_2 Trustworthy_2 Strong_2 Warm_2 Generous_2 Dominant_2 Toughminded_2, pcf
{txt}(obs=741)

Factor analysis/correlation{col 50}Number of obs    = {res}       741
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      18

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.58584      1.11377            0.3694       0.3694
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      1.47207      0.29144            0.2103       0.5797
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      1.18063      0.62712            0.1687       0.7484
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.55350      0.07261            0.0791       0.8274
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.48090      0.10175            0.0687       0.8961
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}      0.37914      0.03122            0.0542       0.9503
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}      0.34793            .            0.0497       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}21{txt}) ={res} 1362.36{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:Competence_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6970}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0107}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3653}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3806}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7532}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0689}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4252}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2472}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7491}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1092}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3606}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2969}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6091}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4277}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5186}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1772}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6212}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3821}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5270}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1903}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4018}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6760}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3715}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2436}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2308}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8182}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2274}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2256}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. rotate, oblique oblimin

{txt}Factor analysis/correlation{col 50}Number of obs    = {res}       741
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: oblique oblimin (Kaiser off){col 50}Number of params =   {res}      18

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |}     Variance   Proportion    Rotated factors are correlated
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.27768       0.3254
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      1.88209       0.2689
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      1.57754       0.2254
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}21{txt}) ={res} 1362.36{txt} Prob>chi2 ={res} 0.0000

{txt}Rotated factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:Competence_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7857}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0036}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0012}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3806}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8711}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0193}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0836}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2472}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8215}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0191}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1021}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2969}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0044}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.9064}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0210}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1772}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0074}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8958}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0259}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1903}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0055}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1415}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8504}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2436}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0037}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1228}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8766}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2256}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

Factor rotation matrix

{space 4}{hline 13}{c  TT}{hline 9}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 7:Factor1}{space 1}{space 1}{ralign 7:Factor2}{space 1}{space 1}{ralign 7:Factor3}{space 1}
{space 4}{hline 13}{c   +}{hline 9}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:Factor1}{space 1}{c |}{space 1}{ralign 7:{res:{sf: 0.8836}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.6759}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.3542}}}{space 1}
{space 4}{space 0}{ralign 12:Factor2}{space 1}{c |}{space 1}{ralign 7:{res:{sf: 0.0144}}}{space 1}{space 1}{ralign 7:{res:{sf:-0.4518}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.8700}}}{space 1}
{space 4}{space 0}{ralign 12:Factor3}{space 1}{c |}{space 1}{ralign 7:{res:{sf:-0.4681}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.5823}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.3429}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 9}{hline 9}{hline 9}

{com}. 
. *** Produces Table SOM.1.b
. mat a = e(r_L)
{txt}
{com}. frmttable using TableSOM1b.rtf , statmat(a) sdec(2\2\2\2\2\2\2\0) replace  ctitles("Item","Component 1 (Competence)","Component 2 (Warmth)","Component 3 (Dominance)") rtitles(Competent\Trustworthy\Strong\Warm\Generous\Dominant\Toughminded\N)  title("Table SOM.1.b: Rotated factor loadings for trait ratings of ideal leader, survey Wave 2") note("N = 741")
{res}{txt:(note: file TableSOM1b.rtf not found)}
{txt}{center:Table SOM.1.b: Rotated factor loadings for trait ratings of ideal leader, survey Wave 2}
{txt}{center:{hline 88}}
{center:{txt}{lalign 13:Item}{txt}{center 26:Component 1 (Competence)}{txt}{center 22:Component 2 (Warmth)}{txt}{center 25:Component 3 (Dominance)}}
{txt}{center:{hline 88}}
{center:{txt}{lalign 13:Competent}{res}{center 26:0.79}{res}{center 22:0.00}{res}{center 25:0.00}}
{center:{txt}{lalign 13:Trustworthy}{res}{center 26:0.87}{res}{center 22:0.02}{res}{center 25:-0.08}}
{center:{txt}{lalign 13:Strong}{res}{center 26:0.82}{res}{center 22:-0.02}{res}{center 25:0.10}}
{center:{txt}{lalign 13:Warm}{res}{center 26:0.00}{res}{center 22:0.91}{res}{center 25:-0.02}}
{center:{txt}{lalign 13:Generous}{res}{center 26:0.01}{res}{center 22:0.90}{res}{center 25:0.03}}
{center:{txt}{lalign 13:Dominant}{res}{center 26:0.01}{res}{center 22:0.14}{res}{center 25:0.85}}
{center:{txt}{lalign 13:Toughminded}{res}{center 26:0.00}{res}{center 22:-0.12}{res}{center 25:0.88}}
{center:{txt}{lalign 13:N}{res}{center 26:}{res}{center 22:}{res}{center 25:}}
{txt}{center:{hline 88}}
{txt}{center:N = 741}


{com}. 
. *** Generates factor score variables (for Wave 2) for robustness tests of main results
. factor Competence_2 Trustworthy_2 Strong_2 Warm_2 Generous_2 Dominant_2 Toughminded_2, pcf
{txt}(obs=741)

Factor analysis/correlation{col 50}Number of obs    = {res}       741
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      18

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.58584      1.11377            0.3694       0.3694
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      1.47207      0.29144            0.2103       0.5797
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      1.18063      0.62712            0.1687       0.7484
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.55350      0.07261            0.0791       0.8274
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.48090      0.10175            0.0687       0.8961
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}      0.37914      0.03122            0.0542       0.9503
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}      0.34793            .            0.0497       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}21{txt}) ={res} 1362.36{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:Competence_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6970}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0107}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3653}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3806}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7532}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0689}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4252}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2472}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7491}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1092}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3606}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2969}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6091}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4277}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5186}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1772}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6212}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3821}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5270}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1903}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4018}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6760}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3715}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2436}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2308}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8182}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2274}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2256}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. rotate, oblique oblimin

{txt}Factor analysis/correlation{col 50}Number of obs    = {res}       741
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: oblique oblimin (Kaiser off){col 50}Number of params =   {res}      18

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |}     Variance   Proportion    Rotated factors are correlated
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.27768       0.3254
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      1.88209       0.2689
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      1.57754       0.2254
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}21{txt}) ={res} 1362.36{txt} Prob>chi2 ={res} 0.0000

{txt}Rotated factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:Competence_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7857}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0036}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0012}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3806}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8711}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0193}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0836}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2472}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8215}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0191}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1021}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2969}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0044}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.9064}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0210}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1772}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0074}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8958}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0259}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1903}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0055}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1415}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8504}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2436}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0037}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1228}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8766}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2256}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

Factor rotation matrix

{space 4}{hline 13}{c  TT}{hline 9}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 7:Factor1}{space 1}{space 1}{ralign 7:Factor2}{space 1}{space 1}{ralign 7:Factor3}{space 1}
{space 4}{hline 13}{c   +}{hline 9}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:Factor1}{space 1}{c |}{space 1}{ralign 7:{res:{sf: 0.8836}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.6759}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.3542}}}{space 1}
{space 4}{space 0}{ralign 12:Factor2}{space 1}{c |}{space 1}{ralign 7:{res:{sf: 0.0144}}}{space 1}{space 1}{ralign 7:{res:{sf:-0.4518}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.8700}}}{space 1}
{space 4}{space 0}{ralign 12:Factor3}{space 1}{c |}{space 1}{ralign 7:{res:{sf:-0.4681}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.5823}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.3429}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 9}{hline 9}{hline 9}

{com}. predict Comp_PCA_2 Warm_PCA_2 Domi_PCA_2
{txt}(option {bf:regression} assumed; regression scoring)

{p 0 0 2}Scoring coefficients (method = regression; based on oblimin(0) rotated factors){p_end}

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:Competence_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.38310}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.00123}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.00427}}}{space 1}
{space 4}{space 0}{ralign 12:Trustworth~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.42526}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.00830}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.06105}}}{space 1}
{space 4}{space 0}{ralign 12:Strong_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.40001}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.01559}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.06241}}}{space 1}
{space 4}{space 0}{ralign 12:Warm_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.00167}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.54622}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.01872}}}{space 1}
{space 4}{space 0}{ralign 12:Generous_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.00042}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.53956}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.01238}}}{space 1}
{space 4}{space 0}{ralign 12:Dominant_2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.00338}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.08076}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.56248}}}{space 1}
{space 4}{space 0}{ralign 12:Toughminde~2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.00330}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.07862}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.58121}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}


{com}. corr Comp_PCA_2 Warm_PCA_2 Domi_PCA_2
{txt}(obs=741)

             {c |} Comp_P~2 Warm_P~2 Domi_P~2
{hline 13}{c +}{hline 27}
  Comp_PCA_2 {c |}{res}   1.0000
  {txt}Warm_PCA_2 {c |}{res}   0.3181   1.0000
  {txt}Domi_PCA_2 {c |}{res}   0.1650   0.0460   1.0000

{txt}
{com}. 
. 
. *** Main outcome variables for Wave 2: Composite scales for dominance, warmth and competence (on 0-1 scales)
. egen Domi_scale_2 = rowmean(Dominant_2 Toughminded_2)
{txt}(306 missing values generated)

{com}. 
. egen Comp_scale_2 = rowmean(Competence_2 Trustworthy_2 Strong_2)
{txt}(291 missing values generated)

{com}. 
. egen Warm_scale_2 = rowmean(Warm_2 Generous_2)
{txt}(305 missing values generated)

{com}. 
. summ Domi_scale_2 Comp_scale_2 Warm_scale_2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Domi_scale_2 {c |}{res}        775    .5288172    .2583886          0          1
{txt}Comp_scale_2 {c |}{res}        790     .906083    .1372004          0          1
{txt}Warm_scale_2 {c |}{res}        776    .6743986    .2480989          0          1
{txt}
{com}. corr Domi_scale_2 Comp_scale_2 Warm_scale_2
{txt}(obs=767)

             {c |} Domi_s~2 Comp_s~2 Warm_s~2
{hline 13}{c +}{hline 27}
Domi_scale_2 {c |}{res}   1.0000
{txt}Comp_scale_2 {c |}{res}   0.1922   1.0000
{txt}Warm_scale_2 {c |}{res}   0.0637   0.3291   1.0000

{txt}
{com}. 
. 
. ******************************************* Self-reported emotional reactions over last week *****************************************************
. * Afraid
. recode w2_q11_1 (8=.), generate(afraid_2)
{txt}(9 differences between {bf:w2_q11_1} and {bf:afraid_2})

{com}. * Frightened
. recode w2_q11_2 (8=.), generate(frightened_2)
{txt}(8 differences between {bf:w2_q11_2} and {bf:frightened_2})

{com}. * Scared
. recode w2_q11_3 (8=.), generate(scared_2)
{txt}(7 differences between {bf:w2_q11_3} and {bf:scared_2})

{com}. 
. ** Composite scale for anxiety
. corr afraid_2 frightened_2 scared_2
{txt}(obs=799)

             {c |} afraid_2 fright~2 scared_2
{hline 13}{c +}{hline 27}
    afraid_2 {c |}{res}   1.0000
{txt}frightened_2 {c |}{res}   0.7676   1.0000
    {txt}scared_2 {c |}{res}   0.7061   0.7152   1.0000

{txt}
{com}. alpha afraid_2 frightened_2 scared_2

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 2.187711
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8899
{txt}
{com}. egen fearfull_scale_W2_7 = rowmean(afraid_2 frightened_2 scared_2)
{txt}(275 missing values generated)

{com}. generate fearfull_scale_2 = (fearfull_scale_W2_7-1)/6
{txt}(275 missing values generated)

{com}. 
. 
. * Angry
. recode w2_q11_4 (8=.), generate(angry_2)
{txt}(9 differences between {bf:w2_q11_4} and {bf:angry_2})

{com}. * Hostile
. recode w2_q11_5 (8=.), generate(hostile_2)
{txt}(10 differences between {bf:w2_q11_5} and {bf:hostile_2})

{com}. * Disgusted
. recode w2_q11_6 (8=.), generate(disgusted_2)
{txt}(12 differences between {bf:w2_q11_6} and {bf:disgusted_2})

{com}. 
. ** Composite scale for agressive emotions
. corr angry_2 hostile_2 disgusted_2
{txt}(obs=795)

             {c |}  angry_2 hostil~2 disgus~2
{hline 13}{c +}{hline 27}
     angry_2 {c |}{res}   1.0000
   {txt}hostile_2 {c |}{res}   0.6010   1.0000
 {txt}disgusted_2 {c |}{res}   0.5372   0.5186   1.0000

{txt}
{com}. alpha angry_2 hostile_2 disgusted_2

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.436327
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7834
{txt}
{com}. egen aggressive_scale_W2_7 = rowmean(angry_2 hostile_2 disgusted_2)
{txt}(277 missing values generated)

{com}. generate aggressive_scale_2 = (aggressive_scale_W2_7-1)/6
{txt}(277 missing values generated)

{com}. 
. 
. * Sad
. recode w2_q11_7 (8=.), generate(sad_2)
{txt}(7 differences between {bf:w2_q11_7} and {bf:sad_2})

{com}. * Lonely
. recode w2_q11_8 (8=.), generate(lonely_2)
{txt}(10 differences between {bf:w2_q11_8} and {bf:lonely_2})

{com}. * Downhearted
. recode w2_q11_9 (8=.), generate(downhearted_2)
{txt}(11 differences between {bf:w2_q11_9} and {bf:downhearted_2})

{com}. 
. ** Composite scale for sadness
. corr sad_2 lonely_2 downhearted_2 
{txt}(obs=797)

             {c |}    sad_2 lonely_2 downhe~2
{hline 13}{c +}{hline 27}
       sad_2 {c |}{res}   1.0000
    {txt}lonely_2 {c |}{res}   0.3580   1.0000
{txt}downhearte~2 {c |}{res}   0.6237   0.4347   1.0000

{txt}
{com}. alpha sad_2 lonely_2 downhearted_2 

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.350082
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7259
{txt}
{com}. egen sadness_scale_W2_7 = rowmean(sad_2 lonely_2 downhearted_2)
{txt}(276 missing values generated)

{com}. generate sadness_scale_2 = (sadness_scale_W2_7-1)/6
{txt}(276 missing values generated)

{com}. 
. 
. * Proud
. recode w2_q11_10 (8=.), generate(proud_2)
{txt}(13 differences between {bf:w2_q11_10} and {bf:proud_2})

{com}. * Strong
. recode w2_q11_11 (8=.), generate(strong_2)
{txt}(11 differences between {bf:w2_q11_11} and {bf:strong_2})

{com}. * Confident
. recode w2_q11_12 (8=.), generate(confident_2)
{txt}(8 differences between {bf:w2_q11_12} and {bf:confident_2})

{com}. 
. ** Composite scale for self-confident emotions
. corr proud_2 strong_2 confident_2
{txt}(obs=793)

             {c |}  proud_2 strong_2 confid~2
{hline 13}{c +}{hline 27}
     proud_2 {c |}{res}   1.0000
    {txt}strong_2 {c |}{res}   0.5883   1.0000
 {txt}confident_2 {c |}{res}   0.5508   0.7228   1.0000

{txt}
{com}. alpha proud_2 strong_2 confident_2

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.534326
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8307
{txt}
{com}. egen selfconf_scale_W2_7 = rowmean(proud_2 strong_2 confident_2)
{txt}(276 missing values generated)

{com}. generate selfconf_scale_2 = (selfconf_scale_W2_7-1)/6
{txt}(276 missing values generated)

{com}. 
. summ fearfull_scale_2 aggressive_scale_2 sadness_scale_2 selfconf_scale_2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
fearfull_s~2 {c |}{res}        806    .4276261    .2618236          0          1
{txt}aggressive~2 {c |}{res}        804    .6732311    .2259084          0          1
{txt}sadness_sc~2 {c |}{res}        805    .5142857    .2271874          0          1
{txt}selfconf_s~2 {c |}{res}        805    .6017253    .2261692          0          1
{txt}
{com}. 
. 
. 
. ******************************************* Self-reported Victimization of Russian Attacks *******************************************************
. **** Recodes all three items
. codebook w2_q8_1 w2_q8_2 w2_q8_3

{txt}{hline}
{res}w2_q8_1{right:8. Please tell us how often the events described below have happened over the la}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:labels12_wave2}

{col 18}Range: [{res}1{txt},{res}6{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}6{col 51}{txt}Missing .: {res}270{txt}/{res}1,081

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}       653{col 32}       1{col 42}{txt}Never
{col 20}{res}        43{col 32}       2{col 42}{txt}Once
{col 20}{res}        37{col 32}       3{col 42}{txt}2 to 4 times
{col 20}{res}        23{col 32}       4{col 42}{txt}5 to 10 times
{col 20}{res}        16{col 32}       5{col 42}{txt}More than 10 times
{col 20}{res}        39{col 32}       6{col 42}{txt}Prefer not to say
{col 20}{res}       270{col 32}       .{col 42}

{txt}{hline}
{res}w2_q8_2{right:8. Please tell us how often the events described below have happened over the la}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:labels12_wave2}

{col 18}Range: [{res}1{txt},{res}6{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}6{col 51}{txt}Missing .: {res}270{txt}/{res}1,081

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}       498{col 32}       1{col 42}{txt}Never
{col 20}{res}        94{col 32}       2{col 42}{txt}Once
{col 20}{res}        92{col 32}       3{col 42}{txt}2 to 4 times
{col 20}{res}        36{col 32}       4{col 42}{txt}5 to 10 times
{col 20}{res}        53{col 32}       5{col 42}{txt}More than 10 times
{col 20}{res}        38{col 32}       6{col 42}{txt}Prefer not to say
{col 20}{res}       270{col 32}       .{col 42}

{txt}{hline}
{res}w2_q8_3{right:8. Please tell us how often the events described below have happened over the la}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:labels12_wave2}

{col 18}Range: [{res}1{txt},{res}6{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}6{col 51}{txt}Missing .: {res}270{txt}/{res}1,081

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}       307{col 32}       1{col 42}{txt}Never
{col 20}{res}       114{col 32}       2{col 42}{txt}Once
{col 20}{res}       146{col 32}       3{col 42}{txt}2 to 4 times
{col 20}{res}        78{col 32}       4{col 42}{txt}5 to 10 times
{col 20}{res}       123{col 32}       5{col 42}{txt}More than 10 times
{col 20}{res}        43{col 32}       6{col 42}{txt}Prefer not to say
{col 20}{res}       270{col 32}       .{col 42}
{txt}
{com}. tab1 w2_q8_1 w2_q8_2 w2_q8_3

{res}-> tabulation of w2_q8_1  

 {txt}8. Please tell us {c |}
     how often the {c |}
  events described {c |}
        below have {c |}
 happened over the {c |}
                la {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
             Never {c |}{res}        653       80.52       80.52
{txt}              Once {c |}{res}         43        5.30       85.82
{txt}      2 to 4 times {c |}{res}         37        4.56       90.38
{txt}     5 to 10 times {c |}{res}         23        2.84       93.22
{txt}More than 10 times {c |}{res}         16        1.97       95.19
{txt} Prefer not to say {c |}{res}         39        4.81      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}        811      100.00

-> tabulation of w2_q8_2  

 {txt}8. Please tell us {c |}
     how often the {c |}
  events described {c |}
        below have {c |}
 happened over the {c |}
                la {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
             Never {c |}{res}        498       61.41       61.41
{txt}              Once {c |}{res}         94       11.59       73.00
{txt}      2 to 4 times {c |}{res}         92       11.34       84.34
{txt}     5 to 10 times {c |}{res}         36        4.44       88.78
{txt}More than 10 times {c |}{res}         53        6.54       95.31
{txt} Prefer not to say {c |}{res}         38        4.69      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}        811      100.00

-> tabulation of w2_q8_3  

 {txt}8. Please tell us {c |}
     how often the {c |}
  events described {c |}
        below have {c |}
 happened over the {c |}
                la {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
             Never {c |}{res}        307       37.85       37.85
{txt}              Once {c |}{res}        114       14.06       51.91
{txt}      2 to 4 times {c |}{res}        146       18.00       69.91
{txt}     5 to 10 times {c |}{res}         78        9.62       79.53
{txt}More than 10 times {c |}{res}        123       15.17       94.70
{txt} Prefer not to say {c |}{res}         43        5.30      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}        811      100.00
{txt}
{com}. recode w2_q8_1 w2_q8_2 w2_q8_3 (6=.)
{txt}(39 changes made to {bf:w2_q8_1})
(38 changes made to {bf:w2_q8_2})
(43 changes made to {bf:w2_q8_3})

{com}. rename w2_q8_1 w2_victim_self
{res}{txt}
{com}. rename w2_q8_2 w2_victim_family
{res}{txt}
{com}. rename w2_q8_3 w2_victim_other
{res}{txt}
{com}. corr w2_victim_self w2_victim_family w2_victim_other
{txt}(obs=752)

             {c |} w2_vic~f w2_vic~y w2_vic~r
{hline 13}{c +}{hline 27}
w2_victim_~f {c |}{res}   1.0000
{txt}w2_victim_~y {c |}{res}   0.4004   1.0000
{txt}w2_victim_~r {c |}{res}   0.2456   0.6305   1.0000

{txt}
{com}. alpha w2_victim_self w2_victim_family w2_victim_other

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .6259643
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.6847
{txt}
{com}. 
. 
. ** Generates victimization scale
. egen Victimization_W2_5 = rowmean(w2_victim_self w2_victim_family w2_victim_other)
{txt}(296 missing values generated)

{com}. generate Victimization_2 = (Victimization_W2_5-1)/4
{txt}(296 missing values generated)

{com}. summ Victimization_2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Victimizat~2 {c |}{res}        785    .2165074    .2450247          0          1
{txt}
{com}. 
. 
. ********************************************* Identification with Ukraine, Russia and Europe *****************************************************
. ** Ukraine
. recode w2_q13_1 (8=.), generate(Ukraine_ID_W2_7)
{txt}(12 differences between {bf:w2_q13_1} and {bf:Ukraine_ID_W2_7})

{com}. recode w2_q14_1 (8=.), generate(Ukraine_close_W2_7)
{txt}(10 differences between {bf:w2_q14_1} and {bf:Ukraine_close_W2_7})

{com}. corr Ukraine_ID_W2_7 Ukraine_close_W2_7
{txt}(obs=797)

             {c |} U~D_W2_7 U~e_W2_7
{hline 13}{c +}{hline 18}
Ukrai~D_W2_7 {c |}{res}   1.0000
{txt}Ukrai~e_W2_7 {c |}{res}   0.7919   1.0000

{txt}
{com}. egen ID_Ukraine_W2_7 = rowmean(Ukraine_ID_W2_7 Ukraine_close_W2_7)
{txt}(278 missing values generated)

{com}. generate ID_Ukraine_2 = (ID_Ukraine_W2_7-1)/6
{txt}(278 missing values generated)

{com}. 
. ** Russia
. recode w2_q13_2 (8=.), generate(Russia_ID_W2_7)
{txt}(15 differences between {bf:w2_q13_2} and {bf:Russia_ID_W2_7})

{com}. recode w2_q14_2 (8=.), generate(Russia_close_W2_7)
{txt}(14 differences between {bf:w2_q14_2} and {bf:Russia_close_W2_7})

{com}. corr Russia_ID_W2_7 Russia_close_W2_7
{txt}(obs=793)

             {c |} R~D_W2_7 R~e_W2_7
{hline 13}{c +}{hline 18}
Russia_I~2_7 {c |}{res}   1.0000
{txt}Russia_c~2_7 {c |}{res}   0.8163   1.0000

{txt}
{com}. egen ID_Russia_W2_7 = rowmean(Russia_ID_W2_7 Russia_close_W2_7)
{txt}(281 missing values generated)

{com}. generate ID_Russia_2 = (ID_Russia_W2_7-1)/6
{txt}(281 missing values generated)

{com}. 
. ** Europe
. recode w2_q13_3 (8=.), generate(Europe_ID_W2_7)
{txt}(21 differences between {bf:w2_q13_3} and {bf:Europe_ID_W2_7})

{com}. recode w2_q14_3 (8=.), generate(Europe_close_W2_7)
{txt}(19 differences between {bf:w2_q14_3} and {bf:Europe_close_W2_7})

{com}. corr Europe_ID_W2_7 Europe_close_W2_7
{txt}(obs=786)

             {c |} E~D_W2_7 E~e_W2_7
{hline 13}{c +}{hline 18}
Europe_I~2_7 {c |}{res}   1.0000
{txt}Europe_c~2_7 {c |}{res}   0.8779   1.0000

{txt}
{com}. egen ID_Europe_W2_7 = rowmean(Europe_ID_W2_7 Europe_close_W2_7)
{txt}(285 missing values generated)

{com}. generate ID_Europe_2 = (ID_Europe_W2_7-1)/6
{txt}(285 missing values generated)

{com}. 
. summ ID_Ukraine_2 ID_Russia_2 ID_Europe_2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
ID_Ukraine_2 {c |}{res}        803    .9432337    .1384083          0          1
{txt}{space 1}ID_Russia_2 {c |}{res}        800    .0653125    .1658194          0          1
{txt}{space 1}ID_Europe_2 {c |}{res}        796    .6871859    .2807245          0          1
{txt}
{com}. 
. 
. 
. ********************************** Creates variable for whether data for all trait rating variables is present ***********************************
. summ Domi_scale_2 Comp_scale_2 Warm_scale_2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Domi_scale_2 {c |}{res}        775    .5288172    .2583886          0          1
{txt}Comp_scale_2 {c |}{res}        790     .906083    .1372004          0          1
{txt}Warm_scale_2 {c |}{res}        776    .6743986    .2480989          0          1
{txt}
{com}. generate include = .
{txt}(1,081 missing values generated)

{com}. replace include = 1 if Domi_scale_1 !=. & Warm_scale_1 !=. & Comp_scale_1 !=. & Domi_scale_2 !=. & Warm_scale_2 !=. & Comp_scale_2 !=.
{txt}(753 real changes made)

{com}. tab include

    {txt}include {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        753      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        753      100.00
{txt}
{com}. 
. ** Creates similar variable based on PCA
. generate include_PCA = .
{txt}(1,081 missing values generated)

{com}. replace include_PCA = 1 if Domi_PCA_1 !=. & Warm_PCA_1 !=. & Comp_PCA_1 !=. & Domi_PCA_2 !=. & Warm_PCA_2 !=. & Comp_PCA_2 !=.
{txt}(704 real changes made)

{com}. tab include_PCA

{txt}include_PCA {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        704      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        704      100.00
{txt}
{com}. 
. 
. ****************************************** Experimental treatment for Ideal Leader Experiment in Wave 2 ******************************************
. * Experimental treatment for leader trait evaluation questions; codes all respondents to be assigned to think of "Conflict, now"
. generate Conflict_2=1
{txt}
{com}. 
. 
. 
. **************************************************************************************************************************************************
. ******************************************************* Difference scores ************************************************************************
. **************************************************************************************************************************************************
. 
. *** Leader trait preferences in ideal leaders
. * Composite scales
. generate Domi_scale_diff = Domi_scale_2 - Domi_scale_1
{txt}(317 missing values generated)

{com}. generate Comp_scale_diff = Comp_scale_2 - Comp_scale_1
{txt}(302 missing values generated)

{com}. generate Warm_scale_diff = Warm_scale_2 - Warm_scale_1
{txt}(316 missing values generated)

{com}. 
. summ Domi_scale_diff Comp_scale_diff Warm_scale_diff

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Domi_scale~f {c |}{res}        764    .0224695    .2304394         -1   .8333333
{txt}Comp_scale~f {c |}{res}        779    .0018186    .1368881  -.8333333   .8333333
{txt}Warm_scale~f {c |}{res}        765    -.046732    .2221718  -.9166666   .8333333
{txt}
{com}. 
. * Single-item traits
. generate Dominance_diff = Dominant_2 - Dominant_1
{txt}(339 missing values generated)

{com}. generate Toughminded_diff = Toughminded_2 - Toughminded_1
{txt}(331 missing values generated)

{com}. 
. generate Competence_diff = Competence_2 - Competence_1
{txt}(328 missing values generated)

{com}. generate Trustworthy_diff = Trustworthy_2 - Trustworthy_1
{txt}(317 missing values generated)

{com}. generate Strong_diff = Strong_2 - Strong_1
{txt}(308 missing values generated)

{com}. 
. generate Warm_diff = Warm_2 - Warm_1
{txt}(331 missing values generated)

{com}. generate Generous_diff = Generous_2 - Generous_1
{txt}(324 missing values generated)

{com}. 
. summ Dominance_diff Toughminded_diff Competence_diff Trustworthy_diff Strong_diff Warm_diff Generous_diff

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Dominance_~f {c |}{res}        742   -.0404313    .2808646         -1          1
{txt}Toughminde~f {c |}{res}        750         .08    .2888164         -1          1
{txt}Competence~f {c |}{res}        753    .0055334     .197705         -1          1
{txt}Trustworth~f {c |}{res}        764   -.0119983    .1558341         -1   .8333333
{txt}{space 1}Strong_diff {c |}{res}        773    .0097025    .1809887         -1   .8333333
{txt}{hline 13}{c +}{hline 57}
{space 3}Warm_diff {c |}{res}        750   -.0431111    .2538692         -1          1
{txt}Generous_d~f {c |}{res}        757   -.0464553    .2611553         -1   .8333333
{txt}
{com}. 
. 
. * Variables based on PCA results
. generate Comp_PCA_diff = Comp_PCA_2 - Comp_PCA_1
{txt}(377 missing values generated)

{com}. generate Warm_PCA_diff = Warm_PCA_2 - Warm_PCA_1
{txt}(377 missing values generated)

{com}. generate Domi_PCA_diff = Domi_PCA_2 - Domi_PCA_1
{txt}(377 missing values generated)

{com}. 
. 
. *** Emotional reactions
. generate fearfull_diff = fearfull_scale_2 - fearfull_scale_1
{txt}(281 missing values generated)

{com}. generate aggressive_diff = aggressive_scale_2 - aggressive_scale_1
{txt}(281 missing values generated)

{com}. 
. generate sadness_diff = sadness_scale_2 - sadness_scale_1
{txt}(280 missing values generated)

{com}. generate selfconf_diff = selfconf_scale_2 - selfconf_scale_1
{txt}(280 missing values generated)

{com}. 
. summ fearfull_diff aggressive_diff sadness_diff selfconf_diff

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
fearfull_d~f {c |}{res}        800   -.0549306    .2021551  -.8333333   .5555556
{txt}aggressive~f {c |}{res}        800    .0198611    .1971963  -.8333333   .7777777
{txt}sadness_diff {c |}{res}        801   -.0074906    .1955764  -.6666666   .7777778
{txt}selfconf_d~f {c |}{res}        801   -.0044736    .1874851  -.6944444          1
{txt}
{com}. 
. *** Identification with Ukraine, Russia and Europe
. generate ID_Ukraine_diff = ID_Ukraine_2  - ID_Ukraine_1
{txt}(284 missing values generated)

{com}. generate ID_Europe_diff = ID_Europe_2 - ID_Europe_1
{txt}(292 missing values generated)

{com}. generate ID_Russia_diff = ID_Russia_2 - ID_Russia_1
{txt}(295 missing values generated)

{com}. 
. summ ID_Ukraine_diff ID_Europe_diff ID_Russia_diff

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
ID_Ukraine~f {c |}{res}        797   -.0003137     .110713  -.8333333          1
{txt}ID_Europe_~f {c |}{res}        789   -.0362273    .2276356         -1          1
{txt}ID_Russia_~f {c |}{res}        786   -.0483461    .1585455       -.75   .6666667
{txt}
{com}. 
. *** Victimization of Russian attacks
. generate Victimization_diff = Victimization_2- Victimization_1
{txt}(310 missing values generated)

{com}. 
. summ Victimization_diff

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Victimizat~f {c |}{res}        771   -.0368029    .2661178         -1   .9166666
{txt}
{com}. 
. 
. 
. 
. 
. 
. **************************************************************************************************************************************************
. ************************************* SOM.2: Descriptive statistics for key variables across waves ***********************************************
. **************************************************************************************************************************************************
. **** The descriptive statistics reported in SOM.2 - and produced below - are NOT printed in a table upon execution of the code below. Instead, one can check all reported descriptive statistics directly in Stata's Results window.
. 
. 
. **** Demographics across waves
. * Wave 1
. tab sex

  {txt}RECODE of {c |}
  w1_q3 (3. {c |}
       Sex) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}        470       43.48       43.48
{txt}     Female {c |}{res}        611       56.52      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,081      100.00
{txt}
{com}. summ age

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}      1,081    35.61332    8.730129         18         55
{txt}
{com}. tab education

    {txt}RECODE of w1_q6 (6. What is the {c |}
highest level of education that you {c |}
                       have complet {c |}      Freq.     Percent        Cum.
{hline 36}{c +}{hline 35}
             Primary or High school {c |}{res}         95        8.86        8.86
{txt}Professional-technical (vocational) {c |}{res}        185       17.26       26.12
{txt}                  Incomplete higher {c |}{res}         85        7.93       34.05
{txt}                    Bachelor degree {c |}{res}        188       17.54       51.59
{txt}          Master degree & Doctorate {c |}{res}        519       48.41      100.00
{txt}{hline 36}{c +}{hline 35}
                              Total {c |}{res}      1,072      100.00
{txt}
{com}. tab region

     {txt}Region {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       East {c |}{res}        137       12.67       12.67
{txt}       West {c |}{res}        171       15.82       28.49
{txt}       Kyiv {c |}{res}        195       18.04       46.53
{txt}      North {c |}{res}        112       10.36       56.89
{txt}     Centre {c |}{res}        273       25.25       82.15
{txt}      South {c |}{res}        193       17.85      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,081      100.00
{txt}
{com}. 
. * wave 2
. tab sex if include == 1

  {txt}RECODE of {c |}
  w1_q3 (3. {c |}
       Sex) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}        308       40.90       40.90
{txt}     Female {c |}{res}        445       59.10      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        753      100.00
{txt}
{com}. summ age if include == 1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        753    36.32537    8.547372         18         54
{txt}
{com}. tab education if include == 1

    {txt}RECODE of w1_q6 (6. What is the {c |}
highest level of education that you {c |}
                       have complet {c |}      Freq.     Percent        Cum.
{hline 36}{c +}{hline 35}
             Primary or High school {c |}{res}         57        7.60        7.60
{txt}Professional-technical (vocational) {c |}{res}        121       16.13       23.73
{txt}                  Incomplete higher {c |}{res}         57        7.60       31.33
{txt}                    Bachelor degree {c |}{res}        134       17.87       49.20
{txt}          Master degree & Doctorate {c |}{res}        381       50.80      100.00
{txt}{hline 36}{c +}{hline 35}
                              Total {c |}{res}        750      100.00
{txt}
{com}. tab region if include == 1

     {txt}Region {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       East {c |}{res}         83       11.02       11.02
{txt}       West {c |}{res}        123       16.33       27.36
{txt}       Kyiv {c |}{res}        143       18.99       46.35
{txt}      North {c |}{res}         78       10.36       56.71
{txt}     Centre {c |}{res}        194       25.76       82.47
{txt}      South {c |}{res}        132       17.53      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        753      100.00
{txt}
{com}. 
. 
. **** Trait rating scales
. * Wave 1
. summ Comp_scale_1 Warm_scale_1 Domi_scale_1 

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Comp_scale_1 {c |}{res}      1,057    .8936718     .145519          0          1
{txt}Warm_scale_1 {c |}{res}      1,055    .7185624    .2263167          0          1
{txt}Domi_scale_1 {c |}{res}      1,051     .512052    .2588131          0          1
{txt}
{com}. alpha Competence_1 Trustworthy_1 Strong_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0169582
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8117
{txt}
{com}. alpha Warm_1 Generous_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0414576
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.8063
{txt}
{com}. alpha Dominant_1 Toughminded_1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  .042429
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.6459
{txt}
{com}. 
. 
. * Wave 2
. summ Comp_scale_2 Warm_scale_2 Domi_scale_2 if include == 1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Comp_scale_2 {c |}{res}        753     .908219     .132078          0          1
{txt}Warm_scale_2 {c |}{res}        753    .6737494    .2455379          0          1
{txt}Domi_scale_2 {c |}{res}        753    .5281098    .2580758          0          1
{txt}
{com}. alpha Competence_2 Trustworthy_2 Strong_2 if include==1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  .012883
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7408
{txt}
{com}. alpha Warm_2 Generous_2 if include==1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}   .04601
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.7738
{txt}
{com}. alpha Dominant_2 Toughminded_2 if include==1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0438885
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.6701
{txt}
{com}. 
. **** Emotional reactions
. * Wave 1
. summ fearfull_scale_1 aggressive_scale_1 sadness_scale_1 selfconf_scale_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
fearfull_s~1 {c |}{res}      1,070    .4757529    .2744772          0          1
{txt}aggressive~1 {c |}{res}      1,070    .6362669    .2370329          0          1
{txt}sadness_sc~1 {c |}{res}      1,074    .5127767    .2336689          0          1
{txt}selfconf_s~1 {c |}{res}      1,070    .6069574    .2299484          0          1
{txt}
{com}. alpha afraid_1 frightened_1 scared_1 if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  2.41545
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8953
{txt}
{com}. alpha sad_1 lonely_1 downhearted_1 if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.404803
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7254
{txt}
{com}. alpha proud_1 strong_1 confident_1 if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.457104
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7866
{txt}
{com}. alpha angry_1 hostile_1 disgusted_1 if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.456529
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7630
{txt}
{com}. 
. * wave 2
. summ fearfull_scale_2 aggressive_scale_2 sadness_scale_2 selfconf_scale_2 if include==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
fearfull_s~2 {c |}{res}        752    .4295582    .2609825          0          1
{txt}aggressive~2 {c |}{res}        750    .6693333    .2272125          0          1
{txt}sadness_sc~2 {c |}{res}        751    .5145362    .2254564          0          1
{txt}selfconf_s~2 {c |}{res}        751    .6013094     .226263          0          1
{txt}
{com}. alpha afraid_2 frightened_2 scared_2 if include==1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  2.18168
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8898
{txt}
{com}. alpha sad_2 lonely_2 downhearted_2 if include==1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.319197
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7195
{txt}
{com}. alpha proud_2 strong_2 confident_2 if include==1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.528404
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8291
{txt}
{com}. alpha angry_2 hostile_2 disgusted_2 if include==1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.449787
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7818
{txt}
{com}. 
. 
. **** Identities
. * Wave 1
. sum ID_Ukraine_1 ID_Europe_1 ID_Russia_1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
ID_Ukraine_1 {c |}{res}      1,064    .9348371    .1526613          0          1
{txt}{space 1}ID_Europe_1 {c |}{res}      1,058    .7063642    .2693415          0          1
{txt}{space 1}ID_Russia_1 {c |}{res}      1,052    .1351394    .2279704          0          1
{txt}
{com}. alpha Ukraine_ID_W1_7 Ukraine_close_W1_7

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .7195476
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.8640
{txt}
{com}. alpha Russia_ID_W1_7 Russia_close_W1_7

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.607426
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.8649
{txt}
{com}. alpha Europe_ID_W1_7 Europe_close_W1_7

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 2.259844
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.8826
{txt}
{com}. 
. * Wave 2
. sum ID_Ukraine_2 ID_Europe_2 ID_Russia_2 if include == 1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
ID_Ukraine_2 {c |}{res}        748     .943516    .1398345          0          1
{txt}{space 1}ID_Europe_2 {c |}{res}        745    .6887025    .2810555          0          1
{txt}{space 1}ID_Russia_2 {c |}{res}        746    .0654602    .1666578          0          1
{txt}
{com}. alpha Ukraine_ID_W2_7 Ukraine_close_W2_7 if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .6294436
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.8920
{txt}
{com}. alpha Russia_ID_W2_7 Russia_close_W2_7 if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .8761365
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.9005
{txt}
{com}. alpha Europe_ID_W2_7 Europe_close_W2_7 if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 2.632705
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.9325
{txt}
{com}. 
. ** Victimization
. summ Victimization_1 

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Victimizat~1 {c |}{res}      1,043    .2472435    .2787995          0          1
{txt}
{com}. alpha w1_victim_self w1_victim_family w1_victim_other

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .8512443
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7277
{txt}
{com}. summ Victimization_2 if include == 1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Victimizat~2 {c |}{res}        738    .2140357    .2394265          0          1
{txt}
{com}. alpha w2_victim_self w2_victim_family w2_victim_other if include == 1

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .5939174
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.6716
{txt}
{com}. 
. 
. 
. **************************************************************************************************************************************************
. **************************************************************** MAIN ANALYSES *******************************************************************
. **************************************************************************************************************************************************
. 
. ********************************************** MAPPING WARTIME LEADER TRAIT PREFERENCES **********************************************************
. *** Key results reported in main text (after reshaping the data-file from wide to longformat below models are produced and reported in SOM.3)
. reg Comp_scale_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 7.13159372       373  .019119554   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 7.13159372       373  .019119554   {txt}Root MSE        =   {res} .13827

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .8989899{col 26}{space 2}   .00715{col 37}{space 1}  125.73{col 46}{space 3}0.000{col 54}{space 4} .8849306{col 67}{space 3} .9130492
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Comp_scale_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 6.52602185       373  .017496037   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 6.52602185       373  .017496037   {txt}Root MSE        =   {res} .13227

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .9091652{col 26}{space 2} .0068397{col 37}{space 1}  132.93{col 46}{space 3}0.000{col 54}{space 4} .8957161{col 67}{space 3} .9226143
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Warm_scale_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 19.7236508       373   .05287842   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 19.7236508       373   .05287842   {txt}Root MSE        =   {res} .22995

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .7034314{col 26}{space 2} .0118906{col 37}{space 1}   59.16{col 46}{space 3}0.000{col 54}{space 4} .6800504{col 67}{space 3} .7268124
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 24.7685853       373  .066403714   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 24.7685853       373  .066403714   {txt}Root MSE        =   {res} .25769

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6691176{col 26}{space 2} .0133248{col 37}{space 1}   50.22{col 46}{space 3}0.000{col 54}{space 4} .6429165{col 67}{space 3} .6953188
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Domi_scale_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 25.3232503       373  .067890751   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 25.3232503       373  .067890751   {txt}Root MSE        =   {res} .26056

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5274064{col 26}{space 2} .0134732{col 37}{space 1}   39.14{col 46}{space 3}0.000{col 54}{space 4} .5009135{col 67}{space 3} .5538993
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 27.3532561       373  .073333126   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 27.3532561       373  .073333126   {txt}Root MSE        =   {res}  .2708

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5211676{col 26}{space 2} .0140028{col 37}{space 1}   37.22{col 46}{space 3}0.000{col 54}{space 4} .4936333{col 67}{space 3} .5487019
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *** Tests differences between traits in wave 1
. ttest Comp_scale_1==Warm_scale_1 if Conflict_1 == 1 & include == 1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
Com~le_1 {c |}{res}{col 12}    374{col 22} .8989899{col 34}   .00715{col 46} .1382735{col 58} .8849306{col 70} .9130492
{txt}Warm~e_1 {c |}{res}{col 12}    374{col 22} .7034314{col 34} .0118906{col 46} .2299531{col 58} .6800504{col 70} .7268124
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    374{col 22} .1955585{col 34} .0117395{col 46} .2270313{col 58} .1724746{col 70} .2186424
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}Comp_scale_1{txt} - {res}Warm_scale_1{txt})               t = {res} 16.6582
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     373

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest Comp_scale_1==Domi_scale_1 if Conflict_1 == 1 & include == 1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
Com~le_1 {c |}{res}{col 12}    374{col 22} .8989899{col 34}   .00715{col 46} .1382735{col 58} .8849306{col 70} .9130492
{txt}Domi~e_1 {c |}{res}{col 12}    374{col 22} .5274064{col 34} .0134732{col 46} .2605585{col 58} .5009135{col 70} .5538993
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    374{col 22} .3715835{col 34} .0133583{col 46} .2583376{col 58} .3453164{col 70} .3978505
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}Comp_scale_1{txt} - {res}Domi_scale_1{txt})               t = {res} 27.8166
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     373

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest Warm_scale_1==Domi_scale_1 if Conflict_1 == 1 & include == 1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
Warm~e_1 {c |}{res}{col 12}    374{col 22} .7034314{col 34} .0118906{col 46} .2299531{col 58} .6800504{col 70} .7268124
{txt}Domi~e_1 {c |}{res}{col 12}    374{col 22} .5274064{col 34} .0134732{col 46} .2605585{col 58} .5009135{col 70} .5538993
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    374{col 22}  .176025{col 34} .0165154{col 46} .3193936{col 58} .1435499{col 70}    .2085
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}Warm_scale_1{txt} - {res}Domi_scale_1{txt})               t = {res} 10.6582
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     373

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. *** Tests differences between traits in wave 2
. ttest Comp_scale_1==Warm_scale_2 if Conflict_2 == 1 & include == 1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
Com~le_1 {c |}{res}{col 12}    753{col 22}  .907887{col 34} .0046071{col 46} .1264234{col 58} .8988426{col 70} .9169313
{txt}Warm_s~2 {c |}{res}{col 12}    753{col 22} .6737494{col 34} .0089479{col 46} .2455379{col 58} .6561836{col 70} .6913153
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    753{col 22} .2341375{col 34} .0094643{col 46} .2597079{col 58}  .215558{col 70} .2527171
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}Comp_scale_1{txt} - {res}Warm_scale_2{txt})               t = {res} 24.7391
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     752

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest Comp_scale_1==Domi_scale_2 if Conflict_2 == 1 & include == 1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
Com~le_1 {c |}{res}{col 12}    753{col 22}  .907887{col 34} .0046071{col 46} .1264234{col 58} .8988426{col 70} .9169313
{txt}Domi_s~2 {c |}{res}{col 12}    753{col 22} .5281098{col 34} .0094048{col 46} .2580758{col 58}  .509647{col 70} .5465726
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    753{col 22} .3797772{col 34} .0097278{col 46} .2669391{col 58} .3606803{col 70}  .398874
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}Comp_scale_1{txt} - {res}Domi_scale_2{txt})               t = {res} 39.0404
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     752

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest Warm_scale_1==Domi_scale_2 if Conflict_2 == 1 & include == 1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
Warm~e_1 {c |}{res}{col 12}    753{col 22} .7201195{col 34}  .008101{col 46} .2222977{col 58} .7042163{col 70} .7360227
{txt}Domi_s~2 {c |}{res}{col 12}    753{col 22} .5281098{col 34} .0094048{col 46} .2580758{col 58}  .509647{col 70} .5465726
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    753{col 22} .1920097{col 34} .0118247{col 46} .3244801{col 58} .1687964{col 70} .2152231
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}Warm_scale_1{txt} - {res}Domi_scale_2{txt})               t = {res} 16.2380
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     752

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. *** Correlations between preferences for same traits across waves
. pwcorr Comp_scale_1 Comp_scale_2 if Conflict_1 == 1, sig

             {txt}{c |} Com~le_1 Comp_s~2
{hline 13}{c +}{hline 18}
Comp_scale_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
Comp_scale_2 {c |} {res}  0.4252   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}

{com}. pwcorr Competence_1 Trustworthy_1 Strong_1 Competence_2 Trustworthy_2 Strong_2 if Conflict_1 == 1, sig

             {txt}{c |} Compet~1 Trustw~1 Strong_1 Compet~2 Trustw~2 Strong_2
{hline 13}{c +}{hline 54}
Competence_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
Trustworth~1 {c |} {res}  0.6473   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
    Strong_1 {c |} {res}  0.5173   0.5964   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
Competence_2 {c |} {res}  0.3561   0.2109   0.2188   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000
             {txt}{c |}
Trustworth~2 {c |} {res}  0.2927   0.3245   0.2561   0.4250   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000   0.0000
             {txt}{c |}
    Strong_2 {c |} {res}  0.2627   0.2959   0.3425   0.4040   0.4687   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000   0.0000   0.0000
             {txt}{c |}

{com}. reg Comp_scale_1 Comp_scale_2 if Conflict_1 == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       389
{txt}{hline 13}{c +}{hline 34}   F(1, 387)       = {res}    85.40
{txt}       Model {c |} {res} 1.32159262         1  1.32159262   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 5.98867591       387  .015474615   {txt}R-squared       ={res}    0.1808
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1787
{txt}       Total {c |} {res} 7.31026853       388  .018840898   {txt}Root MSE        =   {res}  .1244

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Comp_scale_2 {c |}{col 14}{res}{space 2} .4366544{col 26}{space 2} .0472497{col 37}{space 1}    9.24{col 46}{space 3}0.000{col 54}{space 4} .3437562{col 67}{space 3} .5295526
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5007107{col 26}{space 2} .0434152{col 37}{space 1}   11.53{col 46}{space 3}0.000{col 54}{space 4} .4153517{col 67}{space 3} .5860698
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. pwcorr Warm_scale_1 Warm_scale_2 if Conflict_1 == 1, sig

             {txt}{c |} Warm~e_1 Warm_s~2
{hline 13}{c +}{hline 18}
Warm_scale_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
Warm_scale_2 {c |} {res}  0.5508   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}

{com}. pwcorr Generous_1 Warm_1  Generous_2 Warm_2 if Conflict_1 == 1, sig

             {txt}{c |} Genero~1   Warm_1 Genero~2   Warm_2
{hline 13}{c +}{hline 36}
  Generous_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
      Warm_1 {c |} {res}  0.6699   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
  Generous_2 {c |} {res}  0.4936   0.4403   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
      Warm_2 {c |} {res}  0.4482   0.4801   0.6290   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000
             {txt}{c |}

{com}. reg Warm_scale_1 Warm_scale_2 if Conflict_1 == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       382
{txt}{hline 13}{c +}{hline 34}   F(1, 380)       = {res}   165.50
{txt}       Model {c |} {res}  6.0520385         1   6.0520385   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 13.8962587       380  .036569102   {txt}R-squared       ={res}    0.3034
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3016
{txt}       Total {c |} {res} 19.9482972       381  .052357735   {txt}Root MSE        =   {res} .19123

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Warm_scale_2 {c |}{col 14}{res}{space 2} .4847426{col 26}{space 2} .0376806{col 37}{space 1}   12.86{col 46}{space 3}0.000{col 54}{space 4} .4106541{col 67}{space 3} .5588311
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3802865{col 26}{space 2} .0270122{col 37}{space 1}   14.08{col 46}{space 3}0.000{col 54}{space 4} .3271744{col 67}{space 3} .4333986
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. pwcorr Domi_scale_1 Domi_scale_2 if Conflict_1 == 1, sig

             {txt}{c |} Domi~e_1 Domi_s~2
{hline 13}{c +}{hline 18}
Domi_scale_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
Domi_scale_2 {c |} {res}  0.6405   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}

{com}. pwcorr Dominant_1 Toughminded_1 Dominant_2 Toughminded_2 if Conflict_1 == 1, sig

             {txt}{c |} Domina~1 Toughm~1 Domina~2 Toughm~2
{hline 13}{c +}{hline 36}
  Dominant_1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
Toughminde~1 {c |} {res}  0.4859   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
  Dominant_2 {c |} {res}  0.5467   0.4457   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
Toughminde~2 {c |} {res}  0.4058   0.5493   0.5506   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000
             {txt}{c |}

{com}. reg Domi_scale_1 Domi_scale_2 if Conflict_1 == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       379
{txt}{hline 13}{c +}{hline 34}   F(1, 377)       = {res}   262.31
{txt}       Model {c |} {res} 10.5973542         1  10.5973542   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 15.2308481       377  .040400127   {txt}R-squared       ={res}    0.4103
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.4087
{txt}       Total {c |} {res} 25.8282023       378  .068328577   {txt}Root MSE        =   {res}   .201

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Domi_scale_2 {c |}{col 14}{res}{space 2} .6183063{col 26}{space 2} .0381765{col 37}{space 1}   16.20{col 46}{space 3}0.000{col 54}{space 4} .5432407{col 67}{space 3} .6933719
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2019736{col 26}{space 2} .0225106{col 37}{space 1}    8.97{col 46}{space 3}0.000{col 54}{space 4} .1577117{col 67}{space 3} .2462356
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ***** Figure 1 is produced below after reshaping the data-file from wide to long format
. 
. 
. 
. ************************************************** TESTING THE CONFLICT-SENSITIVITY HYPOTHESIS ***************************************************
. **** Tests if thinking about a peaceful future affect trait preferences in a leader in wave 1
. *** Key results reported in main text and full models in SOM.4.a
. reg Comp_scale_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,057
{txt}{hline 13}{c +}{hline 34}   F(1, 1055)      = {res}     6.34
{txt}       Model {c |} {res} .133615968         1  .133615968   {txt}Prob > F        ={res}    0.0119
{txt}    Residual {c |} {res} 22.2280058     1,055    .0210692   {txt}R-squared       ={res}    0.0060
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0050
{txt}       Total {c |} {res} 22.3616218     1,056  .021175778   {txt}Root MSE        =   {res} .14515

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Comp_scale_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .0224865{col 28}{space 2} .0089293{col 39}{space 1}    2.52{col 48}{space 3}0.012{col 56}{space 4} .0049653{col 69}{space 3} .0400077
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .8824179{col 28}{space 2} .0063169{col 39}{space 1}  139.69{col 48}{space 3}0.000{col 56}{space 4} .8700227{col 69}{space 3} .8948131
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(Conflict_1) level(95) 
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,057}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:2.Conflict_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28} Delta-method
{col 16}{c |}      dy/dx{col 28}   std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .0224865{col 28}{space 2} .0089293{col 39}{space 1}    2.52{col 48}{space 3}0.012{col 56}{space 4} .0049653{col 69}{space 3} .0400077
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 80}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(-.1(.05).1)) ylabel(-.1(.05).1) recast(scatter) yline(0) plotopts(mcolor(black) msize(medium)) ciopts(lcolor(black) lwidth(thin)) ///
> xtitle("Peace") ytitle("Marg. Effect of Peace on Competence Importance") title("Competence") scheme(s1mono) legend(off) name(Competence_Fig2, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. reg Warm_scale_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,055
{txt}{hline 13}{c +}{hline 34}   F(1, 1053)      = {res}     7.82
{txt}       Model {c |} {res} .397981906         1  .397981906   {txt}Prob > F        ={res}    0.0053
{txt}    Residual {c |} {res} 53.5871122     1,053  .050889945   {txt}R-squared       ={res}    0.0074
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0064
{txt}       Total {c |} {res} 53.9850941     1,054  .051219254   {txt}Root MSE        =   {res} .22559

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Warm_scale_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .0388455{col 28}{space 2} .0138907{col 39}{space 1}    2.80{col 48}{space 3}0.005{col 56}{space 4} .0115888{col 69}{space 3} .0661021
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .6990476{col 28}{space 2} .0098455{col 39}{space 1}   71.00{col 48}{space 3}0.000{col 56}{space 4} .6797287{col 69}{space 3} .7183666
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(Conflict_1) level(95) 
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,055}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:2.Conflict_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28} Delta-method
{col 16}{c |}      dy/dx{col 28}   std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .0388455{col 28}{space 2} .0138907{col 39}{space 1}    2.80{col 48}{space 3}0.005{col 56}{space 4} .0115888{col 69}{space 3} .0661021
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 80}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(-.1(.05).1)) ylabel(-.1(.05).1) recast(scatter) yline(0) plotopts(mcolor(cranberry) msize(medium)) ciopts(lcolor(cranberry) lwidth(thin)) ///
> xtitle("Peace")  ytitle("Marg. Effect of Peace on Warmth Importance") title("Warmth") scheme(s1mono) legend(off) name(Warmth_Fig2, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. reg Domi_scale_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,051
{txt}{hline 13}{c +}{hline 34}   F(1, 1049)      = {res}     4.41
{txt}       Model {c |} {res} .294218428         1  .294218428   {txt}Prob > F        ={res}    0.0360
{txt}    Residual {c |} {res}  70.039232     1,049  .066767619   {txt}R-squared       ={res}    0.0042
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0032
{txt}       Total {c |} {res} 70.3334504     1,050  .066984238   {txt}Root MSE        =   {res} .25839

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Domi_scale_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0334636{col 28}{space 2} .0159412{col 39}{space 1}   -2.10{col 48}{space 3}0.036{col 56}{space 4}-.0647439{col 69}{space 3}-.0021834
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .5288953{col 28}{space 2} .0113096{col 39}{space 1}   46.77{col 48}{space 3}0.000{col 56}{space 4} .5067032{col 69}{space 3} .5510873
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(Conflict_1) level(95) 
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,051}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:2.Conflict_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28} Delta-method
{col 16}{c |}      dy/dx{col 28}   std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0334636{col 28}{space 2} .0159412{col 39}{space 1}   -2.10{col 48}{space 3}0.036{col 56}{space 4}-.0647439{col 69}{space 3}-.0021834
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 80}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(-.1(.05).1)) ylabel(-.1(.05).1) recast(scatter) yline(0) plotopts(mcolor(navy) msize(medium)) ciopts(lcolor(navy) lwidth(thin)) ///
> xtitle("Peace")  ytitle("Marg. Effect of Peace on Dominance Importance") title("Dominance") scheme(s1mono) legend(off) name(Dominance_Fig2, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. *** Creates Figure 2
. graph combine Competence_Fig2 Warmth_Fig2 Dominance_Fig2, scheme(s1mono) col(3) ysize(3) xsize(6)
{res}{txt}
{com}. graph export Figure2.pdf, replace
{txt}{p 0 4 2}
file {bf}
Figure2.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. *** Creates Table SOM.4.a
. eststo clear
{txt}
{com}. eststo: reg Comp_scale_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,057
{txt}{hline 13}{c +}{hline 34}   F(1, 1055)      = {res}     6.34
{txt}       Model {c |} {res} .133615968         1  .133615968   {txt}Prob > F        ={res}    0.0119
{txt}    Residual {c |} {res} 22.2280058     1,055    .0210692   {txt}R-squared       ={res}    0.0060
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0050
{txt}       Total {c |} {res} 22.3616218     1,056  .021175778   {txt}Root MSE        =   {res} .14515

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Comp_scale_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .0224865{col 28}{space 2} .0089293{col 39}{space 1}    2.52{col 48}{space 3}0.012{col 56}{space 4} .0049653{col 69}{space 3} .0400077
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .8824179{col 28}{space 2} .0063169{col 39}{space 1}  139.69{col 48}{space 3}0.000{col 56}{space 4} .8700227{col 69}{space 3} .8948131
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. eststo: reg Warm_scale_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,055
{txt}{hline 13}{c +}{hline 34}   F(1, 1053)      = {res}     7.82
{txt}       Model {c |} {res} .397981906         1  .397981906   {txt}Prob > F        ={res}    0.0053
{txt}    Residual {c |} {res} 53.5871122     1,053  .050889945   {txt}R-squared       ={res}    0.0074
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0064
{txt}       Total {c |} {res} 53.9850941     1,054  .051219254   {txt}Root MSE        =   {res} .22559

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Warm_scale_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .0388455{col 28}{space 2} .0138907{col 39}{space 1}    2.80{col 48}{space 3}0.005{col 56}{space 4} .0115888{col 69}{space 3} .0661021
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .6990476{col 28}{space 2} .0098455{col 39}{space 1}   71.00{col 48}{space 3}0.000{col 56}{space 4} .6797287{col 69}{space 3} .7183666
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. eststo: reg Domi_scale_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,051
{txt}{hline 13}{c +}{hline 34}   F(1, 1049)      = {res}     4.41
{txt}       Model {c |} {res} .294218428         1  .294218428   {txt}Prob > F        ={res}    0.0360
{txt}    Residual {c |} {res}  70.039232     1,049  .066767619   {txt}R-squared       ={res}    0.0042
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0032
{txt}       Total {c |} {res} 70.3334504     1,050  .066984238   {txt}Root MSE        =   {res} .25839

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Domi_scale_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0334636{col 28}{space 2} .0159412{col 39}{space 1}   -2.10{col 48}{space 3}0.036{col 56}{space 4}-.0647439{col 69}{space 3}-.0021834
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .5288953{col 28}{space 2} .0113096{col 39}{space 1}   46.77{col 48}{space 3}0.000{col 56}{space 4} .5067032{col 69}{space 3} .5510873
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. esttab using TableSOM4a.rtf, se(3) b(3) ar2 onecell label nobaselevels title("Table SOM.4.a: Between-respondent test of the conflict-sensitivity hypothesis") mtitle("Competence scale" "Warmth scale" "Dominance scale") sfmt(0) replace compress star(* 0.05 ** 0.01) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM4a.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM4a.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. 
. 
. ***** Within-subjects test of the conflict-sensitivity hypothesis is conducted after reshaping the data-file (full models from these analyses are reported in SOM.4b below)
. 
. 
. 
. 
. ***************************** TESTING THE EFFECTS OF EMOTIONAL REACTIONS TO THE WAR ON LEADER TRAIT PREFERENCES **********************************
. 
. *** Produces results reported in main text and with full models in Table SOM.5
. eststo clear
{txt}
{com}. eststo: reg Comp_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     4.13
{txt}       Model {c |} {res} .500624175         7  .071517739   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 12.4404021       718  .017326465   {txt}R-squared       ={res}    0.0387
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0293
{txt}       Total {c |} {res} 12.9410263       725  .017849691   {txt}Root MSE        =   {res} .13163

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0581295{col 29}{space 2} .0267534{col 40}{space 1}   -2.17{col 49}{space 3}0.030{col 57}{space 4}-.1106536{col 70}{space 3}-.0056054
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0723606{col 29}{space 2} .0264822{col 40}{space 1}    2.73{col 49}{space 3}0.006{col 57}{space 4} .0203688{col 70}{space 3} .1243524
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0503068{col 29}{space 2} .0274303{col 40}{space 1}    1.83{col 49}{space 3}0.067{col 57}{space 4}-.0035465{col 70}{space 3} .1041601
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2} .0396835{col 29}{space 2}  .027631{col 40}{space 1}    1.44{col 49}{space 3}0.151{col 57}{space 4}-.0145638{col 70}{space 3} .0939307
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .1065099{col 29}{space 2} .0443735{col 40}{space 1}    2.40{col 49}{space 3}0.017{col 57}{space 4} .0193925{col 70}{space 3} .1936273
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}  .014314{col 29}{space 2}  .022299{col 40}{space 1}    0.64{col 49}{space 3}0.521{col 57}{space 4} -.029465{col 70}{space 3} .0580929
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0254169{col 29}{space 2} .0316162{col 40}{space 1}   -0.80{col 49}{space 3}0.422{col 57}{space 4}-.0874881{col 70}{space 3} .0366542
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0031561{col 29}{space 2}  .005406{col 40}{space 1}   -0.58{col 49}{space 3}0.560{col 57}{space 4}-.0137696{col 70}{space 3} .0074574
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. 
. eststo: reg Warm_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     4.46
{txt}       Model {c |} {res} 1.47300002         7  .210428574   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 33.8399859       718    .0471309   {txt}R-squared       ={res}    0.0417
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0324
{txt}       Total {c |} {res} 35.3129859       725  .048707567   {txt}Root MSE        =   {res}  .2171

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0771967{col 29}{space 2} .0441241{col 40}{space 1}   -1.75{col 49}{space 3}0.081{col 57}{space 4}-.1638244{col 70}{space 3}  .009431
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0914454{col 29}{space 2} .0436769{col 40}{space 1}    2.09{col 49}{space 3}0.037{col 57}{space 4} .0056957{col 70}{space 3}  .177195
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .1047498{col 29}{space 2} .0452407{col 40}{space 1}    2.32{col 49}{space 3}0.021{col 57}{space 4}   .01593{col 70}{space 3} .1935697
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0477427{col 29}{space 2} .0455716{col 40}{space 1}   -1.05{col 49}{space 3}0.295{col 57}{space 4}-.1372123{col 70}{space 3} .0417269
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0450279{col 29}{space 2}  .073185{col 40}{space 1}    0.62{col 49}{space 3}0.539{col 57}{space 4}-.0986543{col 70}{space 3}   .18871
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .1459127{col 29}{space 2} .0367775{col 40}{space 1}    3.97{col 49}{space 3}0.000{col 57}{space 4} .0737083{col 70}{space 3}  .218117
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0462364{col 29}{space 2} .0521443{col 40}{space 1}   -0.89{col 49}{space 3}0.376{col 57}{space 4}  -.14861{col 70}{space 3} .0561372
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0490779{col 29}{space 2} .0089161{col 40}{space 1}   -5.50{col 49}{space 3}0.000{col 57}{space 4}-.0665827{col 70}{space 3}-.0315731
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. 
. eststo: reg Domi_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     1.25
{txt}       Model {c |} {res} .443999179         7  .063428454   {txt}Prob > F        ={res}    0.2719
{txt}    Residual {c |} {res} 36.3831826       718  .050672956   {txt}R-squared       ={res}    0.0121
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0024
{txt}       Total {c |} {res} 36.8271818       725  .050796113   {txt}Root MSE        =   {res} .22511

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2} .0067655{col 29}{space 2} .0457521{col 40}{space 1}    0.15{col 49}{space 3}0.882{col 57}{space 4}-.0830584{col 70}{space 3} .0965894
{txt}aggressive_diff {c |}{col 17}{res}{space 2}  .099491{col 29}{space 2} .0452884{col 40}{space 1}    2.20{col 49}{space 3}0.028{col 57}{space 4} .0105775{col 70}{space 3} .1884044
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0315586{col 29}{space 2} .0469099{col 40}{space 1}    0.67{col 49}{space 3}0.501{col 57}{space 4}-.0605383{col 70}{space 3} .1236556
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0005131{col 29}{space 2} .0472531{col 40}{space 1}   -0.01{col 49}{space 3}0.991{col 57}{space 4}-.0932838{col 70}{space 3} .0922576
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0718635{col 29}{space 2} .0758852{col 40}{space 1}    0.95{col 49}{space 3}0.344{col 57}{space 4}-.0771199{col 70}{space 3}  .220847
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}-.0243516{col 29}{space 2} .0381345{col 40}{space 1}   -0.64{col 49}{space 3}0.523{col 57}{space 4}  -.09922{col 70}{space 3} .0505168
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .0678296{col 29}{space 2} .0540683{col 40}{space 1}    1.25{col 49}{space 3}0.210{col 57}{space 4}-.0383212{col 70}{space 3} .1739804
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .0250402{col 29}{space 2} .0092451{col 40}{space 1}    2.71{col 49}{space 3}0.007{col 57}{space 4} .0068895{col 70}{space 3} .0431908
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. 
. esttab using TableSOM5.rtf, se(3) b(3) ar2 onecell label nobaselevels title("Table SOM.5: Relationships between emotional reactions and preferences for leader competence, warmth, and dominance (main analyses)") mtitle("Competence" "Warmth" "Dominance") sfmt(0) replace compress star(* 0.05 ** 0.01 *** 0.001) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM5.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM5.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. 
. *** Creates Figure 3
. * Calculates observed ranges of changes (difference across waves) in fearfull and aggressive emotions in the sample
. sum fearfull_diff if include==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
fearfull_d~f {c |}{res}        748   -.0581922    .2019064  -.8333333   .5555556
{txt}
{com}. sum aggressive_diff if include==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
aggressive~f {c |}{res}        749    .0140558    .1950568  -.8333333   .7777777
{txt}
{com}. 
. *Plot marginal effects for the ranges of changes in our sample
. *Requires instalation of coefplot
. *ssc install coefplot
. *Comp
. reg Comp_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     4.13
{txt}       Model {c |} {res} .500624175         7  .071517739   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 12.4404021       718  .017326465   {txt}R-squared       ={res}    0.0387
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0293
{txt}       Total {c |} {res} 12.9410263       725  .017849691   {txt}Root MSE        =   {res} .13163

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0581295{col 29}{space 2} .0267534{col 40}{space 1}   -2.17{col 49}{space 3}0.030{col 57}{space 4}-.1106536{col 70}{space 3}-.0056054
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0723606{col 29}{space 2} .0264822{col 40}{space 1}    2.73{col 49}{space 3}0.006{col 57}{space 4} .0203688{col 70}{space 3} .1243524
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0503068{col 29}{space 2} .0274303{col 40}{space 1}    1.83{col 49}{space 3}0.067{col 57}{space 4}-.0035465{col 70}{space 3} .1041601
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2} .0396835{col 29}{space 2}  .027631{col 40}{space 1}    1.44{col 49}{space 3}0.151{col 57}{space 4}-.0145638{col 70}{space 3} .0939307
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .1065099{col 29}{space 2} .0443735{col 40}{space 1}    2.40{col 49}{space 3}0.017{col 57}{space 4} .0193925{col 70}{space 3} .1936273
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}  .014314{col 29}{space 2}  .022299{col 40}{space 1}    0.64{col 49}{space 3}0.521{col 57}{space 4} -.029465{col 70}{space 3} .0580929
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0254169{col 29}{space 2} .0316162{col 40}{space 1}   -0.80{col 49}{space 3}0.422{col 57}{space 4}-.0874881{col 70}{space 3} .0366542
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0031561{col 29}{space 2}  .005406{col 40}{space 1}   -0.58{col 49}{space 3}0.560{col 57}{space 4}-.0137696{col 70}{space 3} .0074574
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(c.fearfull_diff=(-.8333333(0.2).5555556)) post
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:726}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.8333333}}
{lalign 7:2._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.6333333}}
{lalign 7:3._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.4333333}}
{lalign 7:4._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.2333333}}
{lalign 7:5._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.0333333}}
{lalign 7:6._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:.1666667}}
{lalign 7:7._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:.3666667}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0465808{col 26}{space 2} .0212673{col 37}{space 1}    2.19{col 46}{space 3}0.029{col 54}{space 4} .0048273{col 67}{space 3} .0883344
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0349549{col 26}{space 2} .0161067{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .0033331{col 67}{space 3} .0665768
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  .023329{col 26}{space 2} .0111271{col 37}{space 1}    2.10{col 46}{space 3}0.036{col 54}{space 4} .0014836{col 67}{space 3} .0451745
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0117031{col 26}{space 2} .0067422{col 37}{space 1}    1.74{col 46}{space 3}0.083{col 54}{space 4}-.0015336{col 67}{space 3} .0249398
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0000772{col 26}{space 2} .0049357{col 37}{space 1}    0.02{col 46}{space 3}0.988{col 54}{space 4}-.0096129{col 67}{space 3} .0097674
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.0115487{col 26}{space 2} .0077798{col 37}{space 1}   -1.48{col 46}{space 3}0.138{col 54}{space 4}-.0268225{col 67}{space 3} .0037252
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.0231746{col 26}{space 2} .0124076{col 37}{space 1}   -1.87{col 46}{space 3}0.062{col 54}{space 4}-.0475341{col 67}{space 3} .0011849
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store comp_fearful
{txt}
{com}. 
. reg Comp_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     4.13
{txt}       Model {c |} {res} .500624175         7  .071517739   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 12.4404021       718  .017326465   {txt}R-squared       ={res}    0.0387
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0293
{txt}       Total {c |} {res} 12.9410263       725  .017849691   {txt}Root MSE        =   {res} .13163

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0581295{col 29}{space 2} .0267534{col 40}{space 1}   -2.17{col 49}{space 3}0.030{col 57}{space 4}-.1106536{col 70}{space 3}-.0056054
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0723606{col 29}{space 2} .0264822{col 40}{space 1}    2.73{col 49}{space 3}0.006{col 57}{space 4} .0203688{col 70}{space 3} .1243524
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0503068{col 29}{space 2} .0274303{col 40}{space 1}    1.83{col 49}{space 3}0.067{col 57}{space 4}-.0035465{col 70}{space 3} .1041601
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2} .0396835{col 29}{space 2}  .027631{col 40}{space 1}    1.44{col 49}{space 3}0.151{col 57}{space 4}-.0145638{col 70}{space 3} .0939307
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .1065099{col 29}{space 2} .0443735{col 40}{space 1}    2.40{col 49}{space 3}0.017{col 57}{space 4} .0193925{col 70}{space 3} .1936273
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}  .014314{col 29}{space 2}  .022299{col 40}{space 1}    0.64{col 49}{space 3}0.521{col 57}{space 4} -.029465{col 70}{space 3} .0580929
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0254169{col 29}{space 2} .0316162{col 40}{space 1}   -0.80{col 49}{space 3}0.422{col 57}{space 4}-.0874881{col 70}{space 3} .0366542
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0031561{col 29}{space 2}  .005406{col 40}{space 1}   -0.58{col 49}{space 3}0.560{col 57}{space 4}-.0137696{col 70}{space 3} .0074574
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(c.aggressive_diff=(-.8333333(0.2).7777777)) post
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:726}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.8333333}}
{lalign 7:2._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.6333333}}
{lalign 7:3._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.4333333}}
{lalign 7:4._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.2333333}}
{lalign 7:5._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.0333333}}
{lalign 7:6._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.1666667}}
{lalign 7:7._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.3666667}}
{lalign 7:8._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.5666667}}
{lalign 7:9._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.7666667}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0600058{col 26}{space 2} .0230719{col 37}{space 1}   -2.60{col 46}{space 3}0.009{col 54}{space 4}-.1053023{col 67}{space 3}-.0147094
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0455337{col 26}{space 2} .0179307{col 37}{space 1}   -2.54{col 46}{space 3}0.011{col 54}{space 4}-.0807365{col 67}{space 3}-.0103309
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0310616{col 26}{space 2} .0129155{col 37}{space 1}   -2.40{col 46}{space 3}0.016{col 54}{space 4}-.0564182{col 67}{space 3} -.005705
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0165895{col 26}{space 2} .0082592{col 37}{space 1}   -2.01{col 46}{space 3}0.045{col 54}{space 4}-.0328045{col 67}{space 3}-.0003745
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0021174{col 26}{space 2} .0050718{col 37}{space 1}   -0.42{col 46}{space 3}0.676{col 54}{space 4}-.0120748{col 67}{space 3}   .00784
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0123548{col 26}{space 2}  .006272{col 37}{space 1}    1.97{col 46}{space 3}0.049{col 54}{space 4} .0000412{col 67}{space 3} .0246683
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .0268269{col 26}{space 2}  .010443{col 37}{space 1}    2.57{col 46}{space 3}0.010{col 54}{space 4} .0063244{col 67}{space 3} .0473293
{txt}{space 10}8  {c |}{col 14}{res}{space 2}  .041299{col 26}{space 2} .0153258{col 37}{space 1}    2.69{col 46}{space 3}0.007{col 54}{space 4} .0112104{col 67}{space 3} .0713877
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0557711{col 26}{space 2} .0204158{col 37}{space 1}    2.73{col 46}{space 3}0.006{col 54}{space 4} .0156893{col 67}{space 3}  .095853
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store comp_aggresive
{txt}
{com}. 
. coefplot (comp_fearful, recast(line) lcolor(green) lwidth(*3) ciopts(recast(rarea) color(green%50)) ylab(-0.15(0.05)0.15) /// 
>             label("{c -(}&Delta{c )-} Fearfull emotions") yline(0, lpattern(dash)))  /// 
>                  (comp_aggresive, recast(line) lcolor(blue) lwidth(*3) ciopts(recast(rarea) color(blue%50)) ylab(-0.15(0.05)0.15) /// 
>                     label("{c -(}&Delta{c )-} Aggressive emotions")), ytitle("Predicted change in {c -(}bf:competence{c )-} preference" " ") scheme(s1mono) at
{res}{txt}
{com}.         graph save comp_fig3, replace
{txt}{p 0 4 2}
(file {bf}
comp_fig3.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:comp_fig3.gph} saved

{com}.                         
. *Warm           
. reg Warm_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     4.46
{txt}       Model {c |} {res} 1.47300002         7  .210428574   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 33.8399859       718    .0471309   {txt}R-squared       ={res}    0.0417
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0324
{txt}       Total {c |} {res} 35.3129859       725  .048707567   {txt}Root MSE        =   {res}  .2171

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0771967{col 29}{space 2} .0441241{col 40}{space 1}   -1.75{col 49}{space 3}0.081{col 57}{space 4}-.1638244{col 70}{space 3}  .009431
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0914454{col 29}{space 2} .0436769{col 40}{space 1}    2.09{col 49}{space 3}0.037{col 57}{space 4} .0056957{col 70}{space 3}  .177195
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .1047498{col 29}{space 2} .0452407{col 40}{space 1}    2.32{col 49}{space 3}0.021{col 57}{space 4}   .01593{col 70}{space 3} .1935697
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0477427{col 29}{space 2} .0455716{col 40}{space 1}   -1.05{col 49}{space 3}0.295{col 57}{space 4}-.1372123{col 70}{space 3} .0417269
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0450279{col 29}{space 2}  .073185{col 40}{space 1}    0.62{col 49}{space 3}0.539{col 57}{space 4}-.0986543{col 70}{space 3}   .18871
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .1459127{col 29}{space 2} .0367775{col 40}{space 1}    3.97{col 49}{space 3}0.000{col 57}{space 4} .0737083{col 70}{space 3}  .218117
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0462364{col 29}{space 2} .0521443{col 40}{space 1}   -0.89{col 49}{space 3}0.376{col 57}{space 4}  -.14861{col 70}{space 3} .0561372
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0490779{col 29}{space 2} .0089161{col 40}{space 1}   -5.50{col 49}{space 3}0.000{col 57}{space 4}-.0665827{col 70}{space 3}-.0315731
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(c.fearfull_diff=(-.8333333(0.2).5555556)) post
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:726}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.8333333}}
{lalign 7:2._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.6333333}}
{lalign 7:3._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.4333333}}
{lalign 7:4._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.2333333}}
{lalign 7:5._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.0333333}}
{lalign 7:6._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:.1666667}}
{lalign 7:7._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:.3666667}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0132382{col 26}{space 2} .0350761{col 37}{space 1}    0.38{col 46}{space 3}0.706{col 54}{space 4}-.0556257{col 67}{space 3} .0821021
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0022011{col 26}{space 2} .0265647{col 37}{space 1}   -0.08{col 46}{space 3}0.934{col 54}{space 4}-.0543548{col 67}{space 3} .0499526
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0176405{col 26}{space 2} .0183518{col 37}{space 1}   -0.96{col 46}{space 3}0.337{col 54}{space 4}  -.05367{col 67}{space 3} .0183891
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0330798{col 26}{space 2} .0111198{col 37}{space 1}   -2.97{col 46}{space 3}0.003{col 54}{space 4} -.054911{col 67}{space 3}-.0112486
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0485191{col 26}{space 2} .0081405{col 37}{space 1}   -5.96{col 46}{space 3}0.000{col 54}{space 4}-.0645011{col 67}{space 3}-.0325372
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.0639585{col 26}{space 2} .0128312{col 37}{space 1}   -4.98{col 46}{space 3}0.000{col 54}{space 4}-.0891496{col 67}{space 3}-.0387674
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.0793978{col 26}{space 2} .0204638{col 37}{space 1}   -3.88{col 46}{space 3}0.000{col 54}{space 4}-.1195738{col 67}{space 3}-.0392218
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store warm_fearfull
{txt}
{com}. 
. reg Warm_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     4.46
{txt}       Model {c |} {res} 1.47300002         7  .210428574   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 33.8399859       718    .0471309   {txt}R-squared       ={res}    0.0417
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0324
{txt}       Total {c |} {res} 35.3129859       725  .048707567   {txt}Root MSE        =   {res}  .2171

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0771967{col 29}{space 2} .0441241{col 40}{space 1}   -1.75{col 49}{space 3}0.081{col 57}{space 4}-.1638244{col 70}{space 3}  .009431
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0914454{col 29}{space 2} .0436769{col 40}{space 1}    2.09{col 49}{space 3}0.037{col 57}{space 4} .0056957{col 70}{space 3}  .177195
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .1047498{col 29}{space 2} .0452407{col 40}{space 1}    2.32{col 49}{space 3}0.021{col 57}{space 4}   .01593{col 70}{space 3} .1935697
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0477427{col 29}{space 2} .0455716{col 40}{space 1}   -1.05{col 49}{space 3}0.295{col 57}{space 4}-.1372123{col 70}{space 3} .0417269
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0450279{col 29}{space 2}  .073185{col 40}{space 1}    0.62{col 49}{space 3}0.539{col 57}{space 4}-.0986543{col 70}{space 3}   .18871
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .1459127{col 29}{space 2} .0367775{col 40}{space 1}    3.97{col 49}{space 3}0.000{col 57}{space 4} .0737083{col 70}{space 3}  .218117
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0462364{col 29}{space 2} .0521443{col 40}{space 1}   -0.89{col 49}{space 3}0.376{col 57}{space 4}  -.14861{col 70}{space 3} .0561372
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0490779{col 29}{space 2} .0089161{col 40}{space 1}   -5.50{col 49}{space 3}0.000{col 57}{space 4}-.0665827{col 70}{space 3}-.0315731
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(c.aggressive_diff=(-.8333333(0.2).7777777)) post
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:726}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.8333333}}
{lalign 7:2._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.6333333}}
{lalign 7:3._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.4333333}}
{lalign 7:4._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.2333333}}
{lalign 7:5._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.0333333}}
{lalign 7:6._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.1666667}}
{lalign 7:7._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.3666667}}
{lalign 7:8._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.5666667}}
{lalign 7:9._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.7666667}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1243505{col 26}{space 2} .0380523{col 37}{space 1}   -3.27{col 46}{space 3}0.001{col 54}{space 4}-.1990576{col 67}{space 3}-.0496434
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1060614{col 26}{space 2} .0295729{col 37}{space 1}   -3.59{col 46}{space 3}0.000{col 54}{space 4}-.1641211{col 67}{space 3}-.0480017
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0877724{col 26}{space 2} .0213014{col 37}{space 1}   -4.12{col 46}{space 3}0.000{col 54}{space 4}-.1295928{col 67}{space 3} -.045952
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0694833{col 26}{space 2} .0136218{col 37}{space 1}   -5.10{col 46}{space 3}0.000{col 54}{space 4}-.0962266{col 67}{space 3}  -.04274
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0511942{col 26}{space 2} .0083649{col 37}{space 1}   -6.12{col 46}{space 3}0.000{col 54}{space 4}-.0676169{col 67}{space 3}-.0347716
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.0329051{col 26}{space 2} .0103443{col 37}{space 1}   -3.18{col 46}{space 3}0.002{col 54}{space 4}-.0532138{col 67}{space 3}-.0125965
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.0146161{col 26}{space 2} .0172235{col 37}{space 1}   -0.85{col 46}{space 3}0.396{col 54}{space 4}-.0484306{col 67}{space 3} .0191984
{txt}{space 10}8  {c |}{col 14}{res}{space 2}  .003673{col 26}{space 2} .0252767{col 37}{space 1}    0.15{col 46}{space 3}0.885{col 54}{space 4} -.045952{col 67}{space 3}  .053298
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0219621{col 26}{space 2} .0336717{col 37}{space 1}    0.65{col 46}{space 3}0.514{col 54}{space 4}-.0441447{col 67}{space 3} .0880688
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store warm_aggressive
{txt}
{com}. 
. coefplot (warm_fearfull, recast(line) lcolor(green) lwidth(*3) ciopts(recast(rarea) color(green%50)) ylab(-0.15(0.05)0.15) /// 
>             label("{c -(}&Delta{c )-} Fearfull emotions") yline(0, lpattern(dash)))  ///  
>                  (warm_aggressive, recast(line) lcolor(blue) lwidth(*3) ciopts(recast(rarea) color(blue%50)) ylab(-0.15(0.05)0.15) /// 
>                     label("{c -(}&Delta{c )-} Aggressive emotions")), ytitle("Predicted change in {c -(}bf:warmth{c )-} preference" " ") scheme(s1mono) at
{res}{txt}
{com}.         graph save warm_fig3, replace
{txt}{p 0 4 2}
(file {bf}
warm_fig3.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:warm_fig3.gph} saved

{com}.         
. *Domi
. reg Domi_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     1.25
{txt}       Model {c |} {res} .443999179         7  .063428454   {txt}Prob > F        ={res}    0.2719
{txt}    Residual {c |} {res} 36.3831826       718  .050672956   {txt}R-squared       ={res}    0.0121
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0024
{txt}       Total {c |} {res} 36.8271818       725  .050796113   {txt}Root MSE        =   {res} .22511

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2} .0067655{col 29}{space 2} .0457521{col 40}{space 1}    0.15{col 49}{space 3}0.882{col 57}{space 4}-.0830584{col 70}{space 3} .0965894
{txt}aggressive_diff {c |}{col 17}{res}{space 2}  .099491{col 29}{space 2} .0452884{col 40}{space 1}    2.20{col 49}{space 3}0.028{col 57}{space 4} .0105775{col 70}{space 3} .1884044
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0315586{col 29}{space 2} .0469099{col 40}{space 1}    0.67{col 49}{space 3}0.501{col 57}{space 4}-.0605383{col 70}{space 3} .1236556
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0005131{col 29}{space 2} .0472531{col 40}{space 1}   -0.01{col 49}{space 3}0.991{col 57}{space 4}-.0932838{col 70}{space 3} .0922576
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0718635{col 29}{space 2} .0758852{col 40}{space 1}    0.95{col 49}{space 3}0.344{col 57}{space 4}-.0771199{col 70}{space 3}  .220847
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}-.0243516{col 29}{space 2} .0381345{col 40}{space 1}   -0.64{col 49}{space 3}0.523{col 57}{space 4}  -.09922{col 70}{space 3} .0505168
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .0678296{col 29}{space 2} .0540683{col 40}{space 1}    1.25{col 49}{space 3}0.210{col 57}{space 4}-.0383212{col 70}{space 3} .1739804
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .0250402{col 29}{space 2} .0092451{col 40}{space 1}    2.71{col 49}{space 3}0.007{col 57}{space 4} .0068895{col 70}{space 3} .0431908
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(c.fearfull_diff=(-.8333333(0.2).5555556)) post
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:726}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.8333333}}
{lalign 7:2._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.6333333}}
{lalign 7:3._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.4333333}}
{lalign 7:4._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.2333333}}
{lalign 7:5._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:-.0333333}}
{lalign 7:6._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:.1666667}}
{lalign 7:7._at: }{space 0}{lalign 13:fearfull_diff} = {res:{ralign 9:.3666667}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0182964{col 26}{space 2} .0363702{col 37}{space 1}    0.50{col 46}{space 3}0.615{col 54}{space 4}-.0531083{col 67}{space 3} .0897011
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0196495{col 26}{space 2} .0275448{col 37}{space 1}    0.71{col 46}{space 3}0.476{col 54}{space 4}-.0344285{col 67}{space 3} .0737275
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0210026{col 26}{space 2} .0190289{col 37}{space 1}    1.10{col 46}{space 3}0.270{col 54}{space 4}-.0163563{col 67}{space 3} .0583615
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0223557{col 26}{space 2} .0115301{col 37}{space 1}    1.94{col 46}{space 3}0.053{col 54}{space 4} -.000281{col 67}{space 3} .0449924
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0237088{col 26}{space 2} .0084408{col 37}{space 1}    2.81{col 46}{space 3}0.005{col 54}{space 4} .0071372{col 67}{space 3} .0402804
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0250619{col 26}{space 2} .0133046{col 37}{space 1}    1.88{col 46}{space 3}0.060{col 54}{space 4}-.0010586{col 67}{space 3} .0511825
{txt}{space 10}7  {c |}{col 14}{res}{space 2}  .026415{col 26}{space 2} .0212188{col 37}{space 1}    1.24{col 46}{space 3}0.214{col 54}{space 4}-.0152433{col 67}{space 3} .0680733
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store domi_fearfull
{txt}
{com}. 
. reg Domi_scale_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       726
{txt}{hline 13}{c +}{hline 34}   F(7, 718)       = {res}     1.25
{txt}       Model {c |} {res} .443999179         7  .063428454   {txt}Prob > F        ={res}    0.2719
{txt}    Residual {c |} {res} 36.3831826       718  .050672956   {txt}R-squared       ={res}    0.0121
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0024
{txt}       Total {c |} {res} 36.8271818       725  .050796113   {txt}Root MSE        =   {res} .22511

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2} .0067655{col 29}{space 2} .0457521{col 40}{space 1}    0.15{col 49}{space 3}0.882{col 57}{space 4}-.0830584{col 70}{space 3} .0965894
{txt}aggressive_diff {c |}{col 17}{res}{space 2}  .099491{col 29}{space 2} .0452884{col 40}{space 1}    2.20{col 49}{space 3}0.028{col 57}{space 4} .0105775{col 70}{space 3} .1884044
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0315586{col 29}{space 2} .0469099{col 40}{space 1}    0.67{col 49}{space 3}0.501{col 57}{space 4}-.0605383{col 70}{space 3} .1236556
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0005131{col 29}{space 2} .0472531{col 40}{space 1}   -0.01{col 49}{space 3}0.991{col 57}{space 4}-.0932838{col 70}{space 3} .0922576
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0718635{col 29}{space 2} .0758852{col 40}{space 1}    0.95{col 49}{space 3}0.344{col 57}{space 4}-.0771199{col 70}{space 3}  .220847
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}-.0243516{col 29}{space 2} .0381345{col 40}{space 1}   -0.64{col 49}{space 3}0.523{col 57}{space 4}  -.09922{col 70}{space 3} .0505168
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .0678296{col 29}{space 2} .0540683{col 40}{space 1}    1.25{col 49}{space 3}0.210{col 57}{space 4}-.0383212{col 70}{space 3} .1739804
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .0250402{col 29}{space 2} .0092451{col 40}{space 1}    2.71{col 49}{space 3}0.007{col 57}{space 4} .0068895{col 70}{space 3} .0431908
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(c.aggressive_diff=(-.8333333(0.2).7777777)) post
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:726}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.8333333}}
{lalign 7:2._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.6333333}}
{lalign 7:3._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.4333333}}
{lalign 7:4._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.2333333}}
{lalign 7:5._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:-.0333333}}
{lalign 7:6._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.1666667}}
{lalign 7:7._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.3666667}}
{lalign 7:8._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.5666667}}
{lalign 7:9._at: }{space 0}{lalign 15:aggressive_diff} = {res:{ralign 9:.7666667}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0611827{col 26}{space 2} .0394563{col 37}{space 1}   -1.55{col 46}{space 3}0.121{col 54}{space 4}-.1386462{col 67}{space 3} .0162808
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0412845{col 26}{space 2}  .030664{col 37}{space 1}   -1.35{col 46}{space 3}0.179{col 54}{space 4}-.1014864{col 67}{space 3} .0189174
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0213863{col 26}{space 2} .0220873{col 37}{space 1}   -0.97{col 46}{space 3}0.333{col 54}{space 4}-.0647498{col 67}{space 3} .0219771
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0014881{col 26}{space 2} .0141244{col 37}{space 1}   -0.11{col 46}{space 3}0.916{col 54}{space 4}-.0292182{col 67}{space 3} .0262419
{txt}{space 10}5  {c |}{col 14}{res}{space 2}   .01841{col 26}{space 2} .0086736{col 37}{space 1}    2.12{col 46}{space 3}0.034{col 54}{space 4} .0013815{col 67}{space 3} .0354386
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0383082{col 26}{space 2}  .010726{col 37}{space 1}    3.57{col 46}{space 3}0.000{col 54}{space 4} .0172502{col 67}{space 3} .0593662
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .0582064{col 26}{space 2}  .017859{col 37}{space 1}    3.26{col 46}{space 3}0.001{col 54}{space 4} .0231443{col 67}{space 3} .0932686
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0781046{col 26}{space 2} .0262093{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} .0266486{col 67}{space 3} .1295606
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0980028{col 26}{space 2}  .034914{col 37}{space 1}    2.81{col 46}{space 3}0.005{col 54}{space 4}  .029457{col 67}{space 3} .1665486
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store domi_aggressive
{txt}
{com}. 
. coefplot (domi_fearfull, recast(line) lcolor(green) lwidth(*3) ciopts(recast(rarea) color(green%50)) ylab(-0.15(0.05)0.15) /// 
>             label("{c -(}&Delta{c )-} Fearfull emotions") yline(0, lpattern(dash)))  /// 
>                  (domi_aggressive, recast(line) lcolor(blue) lwidth(*3) ciopts(recast(rarea) color(blue%50)) ylab(-0.15(0.05)0.15) /// 
>                     label("{c -(}&Delta{c )-} Aggressive emotions")), ytitle("Predicted change in {c -(}bf:dominance{c )-} preference" " ")scheme(s1mono) at
{res}{txt}
{com}.         graph save domi_fig3, replace
{txt}{p 0 4 2}
(file {bf}
domi_fig3.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:domi_fig3.gph} saved

{com}.                                 
. graph combine comp_fig3.gph warm_fig3.gph domi_fig3.gph, ycommon xsize(12) ysize(3) col(3) scale(1.45) scheme(s1mono)
{res}{txt}
{com}. graph export Figure3.pdf, replace
{txt}{p 0 4 2}
file {bf}
Figure3.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. **************************************************************************************************************************************************
. ********************************************************** SUPPLEMENTARY ANALYSES ****************************************************************
. **************************************************************************************************************************************************
. 
. ********************************************** SOM.6. ANALYSES USING SINGLE-ITEM TRAIT VARIABLES *************************************************
. *** SOM 6.a: Average trait importance across survey waves based on single-item trait measures 
. * Wave 1
. eststo clear
{txt}
{com}. eststo: reg Competence_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       363
{txt}{hline 13}{c +}{hline 34}   F(0, 362)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 14.3027241       362  .039510287   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 14.3027241       362  .039510287   {txt}Root MSE        =   {res} .19877

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Competence_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .8797061{col 26}{space 2} .0104328{col 37}{space 1}   84.32{col 46}{space 3}0.000{col 54}{space 4} .8591896{col 67}{space 3} .9002227
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. eststo: reg Trustworthy_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       369
{txt}{hline 13}{c +}{hline 34}   F(0, 368)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 5.52318604       368  .015008658   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 5.52318604       368  .015008658   {txt}Root MSE        =   {res} .12251

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Trustworth~1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2}  .932701{col 26}{space 2} .0063776{col 37}{space 1}  146.25{col 46}{space 3}0.000{col 54}{space 4} .9201599{col 67}{space 3} .9452421
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. eststo: reg Strong_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 9.90411453       373  .026552586   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 9.90411453       373  .026552586   {txt}Root MSE        =   {res} .16295

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Strong_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .8881462{col 26}{space 2} .0084259{col 37}{space 1}  105.41{col 46}{space 3}0.000{col 54}{space 4} .8715779{col 67}{space 3} .9047144
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. 
. eststo: reg Warm_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       371
{txt}{hline 13}{c +}{hline 34}   F(0, 370)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 22.7845154       370  .061579771   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 22.7845154       370  .061579771   {txt}Root MSE        =   {res} .24815

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      Warm_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6922731{col 26}{space 2} .0128834{col 37}{space 1}   53.73{col 46}{space 3}0.000{col 54}{space 4} .6669392{col 67}{space 3} .7176071
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. eststo: reg Generous_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       371
{txt}{hline 13}{c +}{hline 34}   F(0, 370)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 23.1495948       370  .062566472   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 23.1495948       370  .062566472   {txt}Root MSE        =   {res} .25013

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Generous_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .7160827{col 26}{space 2} .0129863{col 37}{space 1}   55.14{col 46}{space 3}0.000{col 54}{space 4} .6905465{col 67}{space 3} .7416188
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{com}. 
. eststo: reg Dominant_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       369
{txt}{hline 13}{c +}{hline 34}   F(0, 368)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 33.3515499       368  .090629212   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 33.3515499       368  .090629212   {txt}Root MSE        =   {res} .30105

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Dominant_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6102078{col 26}{space 2} .0156719{col 37}{space 1}   38.94{col 46}{space 3}0.000{col 54}{space 4} .5793901{col 67}{space 3} .6410254
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{com}. eststo: reg Toughminded_1 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 34.1527031       373  .091562207   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 34.1527031       373  .091562207   {txt}Root MSE        =   {res} .30259

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Toughminde~1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .4465241{col 26}{space 2} .0156467{col 37}{space 1}   28.54{col 46}{space 3}0.000{col 54}{space 4} .4157573{col 67}{space 3} .4772908
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est7{txt} stored)

{com}. 
. esttab using TableSOM6a_wave1.rtf, b(2) ci(2) onecell label nobaselevels title("Table SOM.6.a: Wartime leader trait preferences, Wave 1") mtitle("Competent" "Trustworthy" "Strong" "Warm" "Generous" "Dominant" "Toughminded") modelwidth(4) sfmt(0) replace compress star(* 0.05 ** 0.01 *** 0.001) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM6a_wave1.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM6a_wave1.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. 
. * Wave 2
. eststo clear
{txt}
{com}. eststo: reg Competence_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       373
{txt}{hline 13}{c +}{hline 34}   F(0, 372)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 12.3297588       372  .033144513   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 12.3297588       372  .033144513   {txt}Root MSE        =   {res} .18206

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Competence_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .9008043{col 26}{space 2} .0094265{col 37}{space 1}   95.56{col 46}{space 3}0.000{col 54}{space 4} .8822683{col 67}{space 3} .9193402
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. eststo: reg Trustworthy_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       371
{txt}{hline 13}{c +}{hline 34}   F(0, 370)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 9.78870927       370  .026455971   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 9.78870927       370  .026455971   {txt}Root MSE        =   {res} .16265

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Trustworth~2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .9218329{col 26}{space 2} .0084445{col 37}{space 1}  109.16{col 46}{space 3}0.000{col 54}{space 4} .9052276{col 67}{space 3} .9384382
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. eststo: reg Strong_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       374
{txt}{hline 13}{c +}{hline 34}   F(0, 373)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 9.33571006       373  .025028713   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 9.33571006       373  .025028713   {txt}Root MSE        =   {res}  .1582

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Strong_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .9064171{col 26}{space 2} .0081806{col 37}{space 1}  110.80{col 46}{space 3}0.000{col 54}{space 4} .8903313{col 67}{space 3} .9225029
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. 
. eststo: reg Warm_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       370
{txt}{hline 13}{c +}{hline 34}   F(0, 369)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 29.1673414       369  .079044286   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 29.1673414       369  .079044286   {txt}Root MSE        =   {res} .28115

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      Warm_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6581081{col 26}{space 2} .0146162{col 37}{space 1}   45.03{col 46}{space 3}0.000{col 54}{space 4} .6293666{col 67}{space 3} .6868496
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. eststo: reg Generous_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       372
{txt}{hline 13}{c +}{hline 34}   F(0, 371)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 30.0698915       371  .081050921   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 30.0698915       371  .081050921   {txt}Root MSE        =   {res} .28469

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Generous_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6827957{col 26}{space 2} .0147607{col 37}{space 1}   46.26{col 46}{space 3}0.000{col 54}{space 4} .6537705{col 67}{space 3} .7118209
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{com}. 
. eststo: reg Dominant_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       369
{txt}{hline 13}{c +}{hline 34}   F(0, 368)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 34.2083703       368  .092957528   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 34.2083703       368  .092957528   {txt}Root MSE        =   {res} .30489

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Dominant_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5438121{col 26}{space 2} .0158719{col 37}{space 1}   34.26{col 46}{space 3}0.000{col 54}{space 4} .5126011{col 67}{space 3} .5750231
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{com}. eststo: reg Toughminded_2 if Conflict_1 == 1 & include == 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       372
{txt}{hline 13}{c +}{hline 34}   F(0, 371)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 35.1925771       371  .094858698   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 35.1925771       371  .094858698   {txt}Root MSE        =   {res} .30799

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Toughminde~2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .4977599{col 26}{space 2} .0159686{col 37}{space 1}   31.17{col 46}{space 3}0.000{col 54}{space 4} .4663595{col 67}{space 3} .5291602
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est7{txt} stored)

{com}. 
. esttab using TableSOM6a_wave2.rtf, b(2) ci(2) onecell label nobaselevels title("Table SOM.6.a: Wartime leader trait preferences, Wave 2") mtitle("Competent" "Trustworthy" "Strong" "Warm" "Generous" "Dominant" "Toughminded") modelwidth(4) sfmt(0) replace compress star(* 0.05 ** 0.01 *** 0.001) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM6a_wave2.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM6a_wave2.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. 
. 
. *** Produces Table SOM 6.b (Testing the conflict-sensitivity hypothesis using single-item trait variables)
. eststo clear
{txt}
{com}. eststo: reg Competence_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,024
{txt}{hline 13}{c +}{hline 34}   F(1, 1022)      = {res}    12.13
{txt}       Model {c |} {res} .458968535         1  .458968535   {txt}Prob > F        ={res}    0.0005
{txt}    Residual {c |} {res} 38.6799199     1,022   .03784728   {txt}R-squared       ={res}    0.0117
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0108
{txt}       Total {c |} {res} 39.1388884     1,023  .038258933   {txt}Root MSE        =   {res} .19454

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Competence_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}  .042346{col 28}{space 2} .0121601{col 39}{space 1}    3.48{col 48}{space 3}0.001{col 56}{space 4} .0184843{col 69}{space 3} .0662076
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .8587459{col 28}{space 2} .0086571{col 39}{space 1}   99.20{col 48}{space 3}0.000{col 56}{space 4} .8417582{col 69}{space 3} .8757336
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. 
. eststo: reg Trustworthy_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,045
{txt}{hline 13}{c +}{hline 34}   F(1, 1043)      = {res}     1.03
{txt}       Model {c |} {res} .022871706         1  .022871706   {txt}Prob > F        ={res}    0.3105
{txt}    Residual {c |} {res} 23.1684098     1,043   .02221324   {txt}R-squared       ={res}    0.0010
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 23.1912815     1,044  .022213871   {txt}Root MSE        =   {res} .14904

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} Trustworthy_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}  .009357{col 28}{space 2} .0092213{col 39}{space 1}    1.01{col 48}{space 3}0.310{col 56}{space 4}-.0087375{col 69}{space 3} .0274515
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .9150579{col 28}{space 2} .0065485{col 39}{space 1}  139.74{col 48}{space 3}0.000{col 56}{space 4} .9022082{col 69}{space 3} .9279076
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. 
. eststo: reg Strong_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,055
{txt}{hline 13}{c +}{hline 34}   F(1, 1053)      = {res}     1.85
{txt}       Model {c |} {res} .047997023         1  .047997023   {txt}Prob > F        ={res}    0.1745
{txt}    Residual {c |} {res} 27.3778584     1,053  .025999866   {txt}R-squared       ={res}    0.0018
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0008
{txt}       Total {c |} {res} 27.4258554     1,054  .026020736   {txt}Root MSE        =   {res} .16124

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      Strong_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}   .01349{col 28}{space 2} .0099286{col 39}{space 1}    1.36{col 48}{space 3}0.175{col 56}{space 4}-.0059922{col 69}{space 3} .0329721
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .8772928{col 28}{space 2} .0070239{col 39}{space 1}  124.90{col 48}{space 3}0.000{col 56}{space 4} .8635104{col 69}{space 3} .8910753
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. 
. eststo: reg Warm_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,045
{txt}{hline 13}{c +}{hline 34}   F(1, 1043)      = {res}    10.75
{txt}       Model {c |} {res} .669721507         1  .669721507   {txt}Prob > F        ={res}    0.0011
{txt}    Residual {c |} {res} 65.0050234     1,043  .062325046   {txt}R-squared       ={res}    0.0102
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0092
{txt}       Total {c |} {res} 65.6747449     1,044  .062906844   {txt}Root MSE        =   {res} .24965

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        Warm_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .0506319{col 28}{space 2} .0154457{col 39}{space 1}    3.28{col 48}{space 3}0.001{col 56}{space 4} .0203236{col 69}{space 3} .0809401
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .6836538{col 28}{space 2} .0109479{col 39}{space 1}   62.45{col 48}{space 3}0.000{col 56}{space 4} .6621715{col 69}{space 3} .7051362
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. 
. eststo: reg Generous_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,045
{txt}{hline 13}{c +}{hline 34}   F(1, 1043)      = {res}     2.86
{txt}       Model {c |} {res} .170818797         1  .170818797   {txt}Prob > F        ={res}    0.0911
{txt}    Residual {c |} {res} 62.2984504     1,043  .059730058   {txt}R-squared       ={res}    0.0027
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0018
{txt}       Total {c |} {res} 62.4692692     1,044  .059836465   {txt}Root MSE        =   {res}  .2444

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Generous_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .0255708{col 28}{space 2} .0151207{col 39}{space 1}    1.69{col 48}{space 3}0.091{col 56}{space 4}-.0040997{col 69}{space 3} .0552414
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .7150641{col 28}{space 2} .0107175{col 39}{space 1}   66.72{col 48}{space 3}0.000{col 56}{space 4} .6940337{col 69}{space 3} .7360945
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{com}. 
. eststo: reg Dominant_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,037
{txt}{hline 13}{c +}{hline 34}   F(1, 1035)      = {res}     4.57
{txt}       Model {c |} {res} .406655908         1  .406655908   {txt}Prob > F        ={res}    0.0327
{txt}    Residual {c |} {res} 92.0096065     1,035  .088898171   {txt}R-squared       ={res}    0.0044
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0034
{txt}       Total {c |} {res} 92.4162624     1,036  .089204886   {txt}Root MSE        =   {res} .29816

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Dominant_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0396076{col 28}{space 2} .0185187{col 39}{space 1}   -2.14{col 48}{space 3}0.033{col 56}{space 4}-.0759462{col 69}{space 3} -.003269
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .6140351{col 28}{space 2}  .013164{col 39}{space 1}   46.65{col 48}{space 3}0.000{col 56}{space 4} .5882039{col 69}{space 3} .6398663
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{com}. 
. eststo: reg Toughminded_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,042
{txt}{hline 13}{c +}{hline 34}   F(1, 1040)      = {res}     2.35
{txt}       Model {c |} {res} .208266783         1  .208266783   {txt}Prob > F        ={res}    0.1255
{txt}    Residual {c |} {res} 92.1447924     1,040  .088600762   {txt}R-squared       ={res}    0.0023
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0013
{txt}       Total {c |} {res} 92.3530592     1,041  .088715715   {txt}Root MSE        =   {res} .29766

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} Toughminded_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0282754{col 28}{space 2} .0184424{col 39}{space 1}   -1.53{col 48}{space 3}0.126{col 56}{space 4} -.064464{col 69}{space 3} .0079132
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .4470135{col 28}{space 2} .0130658{col 39}{space 1}   34.21{col 48}{space 3}0.000{col 56}{space 4} .4213752{col 69}{space 3} .4726518
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est7{txt} stored)

{com}. 
. esttab using TableSOM6b.rtf, se(3) b(3) ar2 onecell label nobaselevels title("Table SOM.6.b: Testing the Conflict-Sensitivity Hypothesis using single-item trait measures") mtitle("Competent" "Trustworthy" "Strong" "Warm" "Generous" "Dominant" "Toughminded") modelwidth(4) sfmt(0) replace compress star(* 0.05 ** 0.01 *** 0.001) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM6b.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM6b.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. 
. *** SOM 6.c: Testing the role of emotional reactions (with single-item trait variables)
. eststo clear
{txt}
{com}. eststo: reg Competence_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       709
{txt}{hline 13}{c +}{hline 34}   F(7, 701)       = {res}     1.88
{txt}       Model {c |} {res} .511978013         7  .073139716   {txt}Prob > F        ={res}    0.0696
{txt}    Residual {c |} {res}  27.226699       701  .038839799   {txt}R-squared       ={res}    0.0185
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0087
{txt}       Total {c |} {res}  27.738677       708  .039178922   {txt}Root MSE        =   {res} .19708

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Competence_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0820309{col 29}{space 2} .0405701{col 40}{space 1}   -2.02{col 49}{space 3}0.044{col 57}{space 4}-.1616844{col 70}{space 3}-.0023775
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0246926{col 29}{space 2} .0397871{col 40}{space 1}    0.62{col 49}{space 3}0.535{col 57}{space 4}-.0534236{col 70}{space 3} .1028088
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2}  .073109{col 29}{space 2} .0418207{col 40}{space 1}    1.75{col 49}{space 3}0.081{col 57}{space 4}-.0089998{col 70}{space 3} .1552177
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2} .0326776{col 29}{space 2} .0419613{col 40}{space 1}    0.78{col 49}{space 3}0.436{col 57}{space 4}-.0497073{col 70}{space 3} .1150625
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .1071831{col 29}{space 2} .0667399{col 40}{space 1}    1.61{col 49}{space 3}0.109{col 57}{space 4} -.023851{col 70}{space 3} .2382173
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .0255527{col 29}{space 2} .0335915{col 40}{space 1}    0.76{col 49}{space 3}0.447{col 57}{space 4}-.0403992{col 70}{space 3} .0915047
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0486822{col 29}{space 2} .0480928{col 40}{space 1}   -1.01{col 49}{space 3}0.312{col 57}{space 4}-.1431054{col 70}{space 3} .0457411
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} -.002027{col 29}{space 2} .0082118{col 40}{space 1}   -0.25{col 49}{space 3}0.805{col 57}{space 4}-.0181497{col 70}{space 3} .0140958
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. 
. eststo: reg Trustworthy_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       717
{txt}{hline 13}{c +}{hline 34}   F(7, 709)       = {res}     3.38
{txt}       Model {c |} {res} .538074039         7   .07686772   {txt}Prob > F        ={res}    0.0015
{txt}    Residual {c |} {res} 16.1267334       709  .022745745   {txt}R-squared       ={res}    0.0323
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0227
{txt}       Total {c |} {res} 16.6648074       716  .023274871   {txt}Root MSE        =   {res} .15082

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Trustworthy_d~f{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0525905{col 29}{space 2} .0308852{col 40}{space 1}   -1.70{col 49}{space 3}0.089{col 57}{space 4}-.1132279{col 70}{space 3} .0080469
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0782978{col 29}{space 2} .0305555{col 40}{space 1}    2.56{col 49}{space 3}0.011{col 57}{space 4} .0183076{col 70}{space 3} .1382879
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2}  .051061{col 29}{space 2} .0317049{col 40}{space 1}    1.61{col 49}{space 3}0.108{col 57}{space 4}-.0111858{col 70}{space 3} .1133078
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0036653{col 29}{space 2} .0319384{col 40}{space 1}   -0.11{col 49}{space 3}0.909{col 57}{space 4}-.0663705{col 70}{space 3} .0590399
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2}  .147509{col 29}{space 2} .0517147{col 40}{space 1}    2.85{col 49}{space 3}0.004{col 57}{space 4} .0459767{col 70}{space 3} .2490412
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .0268788{col 29}{space 2} .0257113{col 40}{space 1}    1.05{col 49}{space 3}0.296{col 57}{space 4}-.0236007{col 70}{space 3} .0773583
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .0378519{col 29}{space 2} .0364933{col 40}{space 1}    1.04{col 49}{space 3}0.300{col 57}{space 4}-.0337959{col 70}{space 3} .1094997
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0135873{col 29}{space 2} .0062387{col 40}{space 1}   -2.18{col 49}{space 3}0.030{col 57}{space 4} -.025836{col 70}{space 3}-.0013387
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. 
. eststo: reg Strong_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       725
{txt}{hline 13}{c +}{hline 34}   F(7, 717)       = {res}     3.98
{txt}       Model {c |} {res}  .84250916         7  .120358451   {txt}Prob > F        ={res}    0.0003
{txt}    Residual {c |} {res} 21.6568776       717   .03020485   {txt}R-squared       ={res}    0.0374
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0280
{txt}       Total {c |} {res} 22.4993868       724  .031076501   {txt}Root MSE        =   {res}  .1738

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Strong_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0497466{col 29}{space 2} .0353671{col 40}{space 1}   -1.41{col 49}{space 3}0.160{col 57}{space 4} -.119182{col 70}{space 3} .0196887
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0978924{col 29}{space 2} .0352371{col 40}{space 1}    2.78{col 49}{space 3}0.006{col 57}{space 4} .0287121{col 70}{space 3} .1670728
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0480876{col 29}{space 2} .0362421{col 40}{space 1}    1.33{col 49}{space 3}0.185{col 57}{space 4}-.0230657{col 70}{space 3} .1192408
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2} .0913268{col 29}{space 2} .0369648{col 40}{space 1}    2.47{col 49}{space 3}0.014{col 57}{space 4} .0187546{col 70}{space 3}  .163899
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0855933{col 29}{space 2} .0586684{col 40}{space 1}    1.46{col 49}{space 3}0.145{col 57}{space 4}-.0295892{col 70}{space 3} .2007758
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}-.0029368{col 29}{space 2} .0294678{col 40}{space 1}   -0.10{col 49}{space 3}0.921{col 57}{space 4}-.0607903{col 70}{space 3} .0549166
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0591288{col 29}{space 2} .0420704{col 40}{space 1}   -1.41{col 49}{space 3}0.160{col 57}{space 4}-.1417246{col 70}{space 3} .0234671
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .0057704{col 29}{space 2} .0071378{col 40}{space 1}    0.81{col 49}{space 3}0.419{col 57}{space 4}-.0082431{col 70}{space 3}  .019784
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. 
. eststo: reg Warm_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       716
{txt}{hline 13}{c +}{hline 34}   F(7, 708)       = {res}     3.76
{txt}       Model {c |} {res} 1.61252579         7  .230360828   {txt}Prob > F        ={res}    0.0005
{txt}    Residual {c |} {res} 43.4207976       708   .06132881   {txt}R-squared       ={res}    0.0358
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0263
{txt}       Total {c |} {res} 45.0333234       715  .062983669   {txt}Root MSE        =   {res} .24765

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      Warm_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.1045381{col 29}{space 2} .0505709{col 40}{space 1}   -2.07{col 49}{space 3}0.039{col 57}{space 4}-.2038249{col 70}{space 3}-.0052513
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0844083{col 29}{space 2} .0501873{col 40}{space 1}    1.68{col 49}{space 3}0.093{col 57}{space 4}-.0141255{col 70}{space 3} .1829421
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .1436926{col 29}{space 2} .0525139{col 40}{space 1}    2.74{col 49}{space 3}0.006{col 57}{space 4}  .040591{col 70}{space 3} .2467942
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0209114{col 29}{space 2} .0524423{col 40}{space 1}   -0.40{col 49}{space 3}0.690{col 57}{space 4}-.1238724{col 70}{space 3} .0820496
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0618101{col 29}{space 2}  .084831{col 40}{space 1}    0.73{col 49}{space 3}0.466{col 57}{space 4}-.1047402{col 70}{space 3} .2283605
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .1390536{col 29}{space 2} .0434035{col 40}{space 1}    3.20{col 49}{space 3}0.001{col 57}{space 4} .0538386{col 70}{space 3} .2242686
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .0162804{col 29}{space 2} .0603714{col 40}{space 1}    0.27{col 49}{space 3}0.787{col 57}{space 4} -.102248{col 70}{space 3} .1348087
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0437774{col 29}{space 2} .0102851{col 40}{space 1}   -4.26{col 49}{space 3}0.000{col 57}{space 4}-.0639704{col 70}{space 3}-.0235845
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. 
. eststo: reg Generous_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       720
{txt}{hline 13}{c +}{hline 34}   F(7, 712)       = {res}     2.56
{txt}       Model {c |} {res} 1.20632126         7  .172331609   {txt}Prob > F        ={res}    0.0131
{txt}    Residual {c |} {res} 47.9538226       712  .067350874   {txt}R-squared       ={res}    0.0245
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0149
{txt}       Total {c |} {res} 49.1601439       719   .06837294   {txt}Root MSE        =   {res} .25952

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Generous_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0482396{col 29}{space 2} .0533254{col 40}{space 1}   -0.90{col 49}{space 3}0.366{col 57}{space 4}-.1529335{col 70}{space 3} .0564543
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0826283{col 29}{space 2} .0523894{col 40}{space 1}    1.58{col 49}{space 3}0.115{col 57}{space 4}-.0202279{col 70}{space 3} .1854846
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0848324{col 29}{space 2} .0543814{col 40}{space 1}    1.56{col 49}{space 3}0.119{col 57}{space 4}-.0219346{col 70}{space 3} .1915994
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0578568{col 29}{space 2} .0549396{col 40}{space 1}   -1.05{col 49}{space 3}0.293{col 57}{space 4}-.1657198{col 70}{space 3} .0500063
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2}  .018716{col 29}{space 2} .0875917{col 40}{space 1}    0.21{col 49}{space 3}0.831{col 57}{space 4}-.1532528{col 70}{space 3} .1906849
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .1319676{col 29}{space 2} .0441594{col 40}{space 1}    2.99{col 49}{space 3}0.003{col 57}{space 4} .0452694{col 70}{space 3} .2186657
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.0928652{col 29}{space 2}  .062403{col 40}{space 1}   -1.49{col 49}{space 3}0.137{col 57}{space 4} -.215381{col 70}{space 3} .0296506
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0509659{col 29}{space 2} .0107097{col 40}{space 1}   -4.76{col 49}{space 3}0.000{col 57}{space 4}-.0719922{col 70}{space 3}-.0299395
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{com}. 
. eststo: reg Dominance_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       709
{txt}{hline 13}{c +}{hline 34}   F(7, 701)       = {res}     0.75
{txt}       Model {c |} {res}  .41743423         7  .059633461   {txt}Prob > F        ={res}    0.6280
{txt}    Residual {c |} {res} 55.5947869       701  .079307827   {txt}R-squared       ={res}    0.0075
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0025
{txt}       Total {c |} {res} 56.0122212       708  .079113307   {txt}Root MSE        =   {res} .28162

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} Dominance_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.0079294{col 29}{space 2} .0576976{col 40}{space 1}   -0.14{col 49}{space 3}0.891{col 57}{space 4}-.1212102{col 70}{space 3} .1053515
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .0016055{col 29}{space 2} .0576353{col 40}{space 1}    0.03{col 49}{space 3}0.978{col 57}{space 4}-.1115529{col 70}{space 3}  .114764
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .1055763{col 29}{space 2} .0596606{col 40}{space 1}    1.77{col 49}{space 3}0.077{col 57}{space 4}-.0115587{col 70}{space 3} .2227112
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.0196567{col 29}{space 2} .0606294{col 40}{space 1}   -0.32{col 49}{space 3}0.746{col 57}{space 4}-.1386936{col 70}{space 3} .0993802
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .1119102{col 29}{space 2} .0961817{col 40}{space 1}    1.16{col 49}{space 3}0.245{col 57}{space 4}-.0769286{col 70}{space 3}  .300749
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}-.0079579{col 29}{space 2} .0487477{col 40}{space 1}   -0.16{col 49}{space 3}0.870{col 57}{space 4}-.1036668{col 70}{space 3}  .087751
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .0270551{col 29}{space 2} .0686863{col 40}{space 1}    0.39{col 49}{space 3}0.694{col 57}{space 4}-.1078004{col 70}{space 3} .1619106
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} -.036551{col 29}{space 2} .0116822{col 40}{space 1}   -3.13{col 49}{space 3}0.002{col 57}{space 4}-.0594874{col 70}{space 3}-.0136146
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{com}. 
. eststo: reg Toughminded_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       721
{txt}{hline 13}{c +}{hline 34}   F(7, 713)       = {res}     2.11
{txt}       Model {c |} {res} 1.20497034         7   .17213862   {txt}Prob > F        ={res}    0.0405
{txt}    Residual {c |} {res} 58.1900809       713  .081613017   {txt}R-squared       ={res}    0.0203
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0107
{txt}       Total {c |} {res} 59.3950513       720  .082493127   {txt}Root MSE        =   {res} .28568

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Toughminded_d~f{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2} .0002145{col 29}{space 2} .0582912{col 40}{space 1}    0.00{col 49}{space 3}0.997{col 57}{space 4}-.1142284{col 70}{space 3} .1146575
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .1822095{col 29}{space 2}  .057533{col 40}{space 1}    3.17{col 49}{space 3}0.002{col 57}{space 4} .0692553{col 70}{space 3} .2951638
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2}-.0252569{col 29}{space 2} .0596021{col 40}{space 1}   -0.42{col 49}{space 3}0.672{col 57}{space 4}-.1422735{col 70}{space 3} .0917598
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}  .041708{col 29}{space 2} .0601011{col 40}{space 1}    0.69{col 49}{space 3}0.488{col 57}{space 4}-.0762883{col 70}{space 3} .1597043
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .0638153{col 29}{space 2} .0972792{col 40}{space 1}    0.66{col 49}{space 3}0.512{col 57}{space 4}-.1271726{col 70}{space 3} .2548032
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}-.0183312{col 29}{space 2} .0486229{col 40}{space 1}   -0.38{col 49}{space 3}0.706{col 57}{space 4}-.1137924{col 70}{space 3} .0771299
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .1002133{col 29}{space 2} .0701895{col 40}{space 1}    1.43{col 49}{space 3}0.154{col 57}{space 4}-.0375895{col 70}{space 3} .2380161
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .0833837{col 29}{space 2}   .01177{col 40}{space 1}    7.08{col 49}{space 3}0.000{col 57}{space 4} .0602758{col 70}{space 3} .1064917
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est7{txt} stored)

{com}. 
. esttab using TableSOM6c.rtf, se(3) b(3) ar2 onecell label nobaselevels title("Table SOM.6.c: Relationships between emotional reactions and preferences for leader traits using single-item trait variables") mtitle("Competent" "Trustworthy" "Strong" "Warm" "Generous" "Dominant" "Toughminded") modelwidth(4) sfmt(0) replace compress star(* 0.05 ** 0.01 *** 0.001) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM6c.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM6c.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. 
. 
. ********************************************** SOM.7. ANALYSES USING PCA FACTOR SCORES AS TRAIT VARIABLES *************************************************
. 
. *** SOM 7.a: Testing the Conflict-Sensitivity Hypothesis and effect of emotional reactions using factor score variables for trait dimensions
. ** Between-respondent analyses
. eststo clear
{txt}
{com}. 
. eststo: reg Comp_PCA_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       988
{txt}{hline 13}{c +}{hline 34}   F(1, 986)       = {res}     3.54
{txt}       Model {c |} {res}  3.5308171         1   3.5308171   {txt}Prob > F        ={res}    0.0602
{txt}    Residual {c |} {res} 983.469185       986   .99743325   {txt}R-squared       ={res}    0.0036
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0026
{txt}       Total {c |} {res} 987.000002       987           1   {txt}Root MSE        =   {res} .99872

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Comp_PCA_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .1195807{col 28}{space 2} .0635573{col 39}{space 1}    1.88{col 48}{space 3}0.060{col 56}{space 4}-.0051424{col 69}{space 3} .2443039
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.0608797{col 28}{space 2} .0453493{col 39}{space 1}   -1.34{col 48}{space 3}0.180{col 56}{space 4} -.149872{col 69}{space 3} .0281127
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. 
. eststo: reg Warm_PCA_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       988
{txt}{hline 13}{c +}{hline 34}   F(1, 986)       = {res}     5.42
{txt}       Model {c |} {res} 5.39956244         1  5.39956244   {txt}Prob > F        ={res}    0.0201
{txt}    Residual {c |} {res} 981.600435       986  .995537967   {txt}R-squared       ={res}    0.0055
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0045
{txt}       Total {c |} {res} 986.999998       987  .999999998   {txt}Root MSE        =   {res} .99777

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Warm_PCA_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2} .1478778{col 28}{space 2} .0634969{col 39}{space 1}    2.33{col 48}{space 3}0.020{col 56}{space 4} .0232732{col 69}{space 3} .2724824
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} -.075286{col 28}{space 2} .0453062{col 39}{space 1}   -1.66{col 48}{space 3}0.097{col 56}{space 4}-.1641937{col 69}{space 3} .0136218
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. 
. eststo: reg Domi_PCA_1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       988
{txt}{hline 13}{c +}{hline 34}   F(1, 986)       = {res}     5.89
{txt}       Model {c |} {res} 5.86408685         1  5.86408685   {txt}Prob > F        ={res}    0.0154
{txt}    Residual {c |} {res} 981.135917       986  .995066853   {txt}R-squared       ={res}    0.0059
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0049
{txt}       Total {c |} {res} 987.000004       987           1   {txt}Root MSE        =   {res} .99753

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    Domi_PCA_1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.1541075{col 28}{space 2} .0634819{col 39}{space 1}   -2.43{col 48}{space 3}0.015{col 56}{space 4}-.2786826{col 69}{space 3}-.0295324
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .0784576{col 28}{space 2} .0452955{col 39}{space 1}    1.73{col 48}{space 3}0.084{col 56}{space 4}-.0104291{col 69}{space 3} .1673443
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. 
. esttab using TableSOM7a1.rtf, se(3) b(3) ar2 onecell label nobaselevels title("Table SOM.7.a.1: Between-respondent test of the Conflict-Sensitivity Hypothesis using factor scores") mtitle("Competence (PCA)" "Warmth (PCA)" "Dominance (PCA)") modelwidth() sfmt(0) replace compress star(* 0.05 ** 0.01 *** 0.001) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM7a1.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM7a1.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. 
. ** Within-respondent analyses: see below after dataset is reshaped to long format (reported as Table 7.a.2)
. 
. 
. *** SOM. 7.b: Testing the role of emotional reactions to the war using factor score trait variables
. eststo clear
{txt}
{com}. 
. eststo: reg Comp_PCA_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include_PCA==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       683
{txt}{hline 13}{c +}{hline 34}   F(7, 675)       = {res}     4.23
{txt}       Model {c |} {res} 25.1549777         7  3.59356824   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 573.196918       675   .84918062   {txt}R-squared       ={res}    0.0420
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0321
{txt}       Total {c |} {res} 598.351896       682  .877348821   {txt}Root MSE        =   {res} .92151

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Comp_PCA_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}  -.36725{col 29}{space 2} .1938587{col 40}{space 1}   -1.89{col 49}{space 3}0.059{col 57}{space 4}-.7478886{col 70}{space 3} .0133885
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .4766669{col 29}{space 2} .1914833{col 40}{space 1}    2.49{col 49}{space 3}0.013{col 57}{space 4} .1006924{col 70}{space 3} .8526414
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .5060609{col 29}{space 2} .2007945{col 40}{space 1}    2.52{col 49}{space 3}0.012{col 57}{space 4}  .111804{col 70}{space 3} .9003177
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2} .3134092{col 29}{space 2} .2031848{col 40}{space 1}    1.54{col 49}{space 3}0.123{col 57}{space 4}-.0855409{col 70}{space 3} .7123594
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .7697779{col 29}{space 2}  .328259{col 40}{space 1}    2.35{col 49}{space 3}0.019{col 57}{space 4} .1252464{col 70}{space 3} 1.414309
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .1507477{col 29}{space 2} .1640946{col 40}{space 1}    0.92{col 49}{space 3}0.359{col 57}{space 4}-.1714495{col 70}{space 3} .4729449
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2} .0235283{col 29}{space 2} .2386548{col 40}{space 1}    0.10{col 49}{space 3}0.921{col 57}{space 4}-.4450667{col 70}{space 3} .4921234
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0873647{col 29}{space 2} .0391463{col 40}{space 1}   -2.23{col 49}{space 3}0.026{col 57}{space 4} -.164228{col 70}{space 3}-.0105015
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. 
. eststo: reg Warm_PCA_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include_PCA==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       683
{txt}{hline 13}{c +}{hline 34}   F(7, 675)       = {res}     3.38
{txt}       Model {c |} {res} 19.6506272         7  2.80723245   {txt}Prob > F        ={res}    0.0015
{txt}    Residual {c |} {res} 560.722134       675  .830699458   {txt}R-squared       ={res}    0.0339
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0238
{txt}       Total {c |} {res} 580.372761       682  .850986453   {txt}Root MSE        =   {res} .91143

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Warm_PCA_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2}-.2765789{col 29}{space 2} .1917376{col 40}{space 1}   -1.44{col 49}{space 3}0.150{col 57}{space 4}-.6530526{col 70}{space 3} .0998949
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .2124068{col 29}{space 2} .1893882{col 40}{space 1}    1.12{col 49}{space 3}0.262{col 57}{space 4}-.1594539{col 70}{space 3} .5842675
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .5518197{col 29}{space 2} .1985974{col 40}{space 1}    2.78{col 49}{space 3}0.006{col 57}{space 4} .1618766{col 70}{space 3} .9417627
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2}-.1436557{col 29}{space 2} .2009616{col 40}{space 1}   -0.71{col 49}{space 3}0.475{col 57}{space 4}-.5382408{col 70}{space 3} .2509293
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .2350241{col 29}{space 2} .3246673{col 40}{space 1}    0.72{col 49}{space 3}0.469{col 57}{space 4}-.4024552{col 70}{space 3} .8725034
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2} .5211665{col 29}{space 2} .1622991{col 40}{space 1}    3.21{col 49}{space 3}0.001{col 57}{space 4} .2024947{col 70}{space 3} .8398383
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}-.1254654{col 29}{space 2} .2360435{col 40}{space 1}   -0.53{col 49}{space 3}0.595{col 57}{space 4}-.5889333{col 70}{space 3} .3380024
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0162903{col 29}{space 2}  .038718{col 40}{space 1}   -0.42{col 49}{space 3}0.674{col 57}{space 4}-.0923125{col 70}{space 3} .0597319
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. 
. eststo: reg Domi_PCA_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include_PCA==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       683
{txt}{hline 13}{c +}{hline 34}   F(7, 675)       = {res}     1.58
{txt}       Model {c |} {res} 8.22526711         7  1.17503816   {txt}Prob > F        ={res}    0.1390
{txt}    Residual {c |} {res} 502.920666       675  .745067653   {txt}R-squared       ={res}    0.0161
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0059
{txt}       Total {c |} {res} 511.145933       682   .74948084   {txt}Root MSE        =   {res} .86317

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Domi_PCA_diff{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fearfull_diff {c |}{col 17}{res}{space 2} .0987235{col 29}{space 2} .1815863{col 40}{space 1}    0.54{col 49}{space 3}0.587{col 57}{space 4}-.2578185{col 70}{space 3} .4552654
{txt}aggressive_diff {c |}{col 17}{res}{space 2} .3819084{col 29}{space 2} .1793613{col 40}{space 1}    2.13{col 49}{space 3}0.034{col 57}{space 4} .0297353{col 70}{space 3} .7340816
{txt}{space 3}sadness_diff {c |}{col 17}{res}{space 2} .0433761{col 29}{space 2}  .188083{col 40}{space 1}    0.23{col 49}{space 3}0.818{col 57}{space 4} -.325922{col 70}{space 3} .4126741
{txt}{space 2}selfconf_diff {c |}{col 17}{res}{space 2} .1620485{col 29}{space 2}  .190322{col 40}{space 1}    0.85{col 49}{space 3}0.395{col 57}{space 4}-.2116458{col 70}{space 3} .5357428
{txt}ID_Ukraine_diff {c |}{col 17}{res}{space 2} .2895549{col 29}{space 2} .3074783{col 40}{space 1}    0.94{col 49}{space 3}0.347{col 57}{space 4}-.3141741{col 70}{space 3} .8932838
{txt}{space 1}ID_Europe_diff {c |}{col 17}{res}{space 2}-.0105256{col 29}{space 2} .1537064{col 40}{space 1}   -0.07{col 49}{space 3}0.945{col 57}{space 4}-.3123258{col 70}{space 3} .2912746
{txt}{space 1}ID_Russia_diff {c |}{col 17}{res}{space 2}  .331105{col 29}{space 2} .2235466{col 40}{space 1}    1.48{col 49}{space 3}0.139{col 57}{space 4}-.1078252{col 70}{space 3} .7700353
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .0707088{col 29}{space 2} .0366681{col 40}{space 1}    1.93{col 49}{space 3}0.054{col 57}{space 4}-.0012885{col 70}{space 3} .1427061
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. 
. esttab using TableSOM7b.rtf, se(3) b(3) ar2 onecell label nobaselevels title("Table SOM.7.b: Relationships between emotional reactions and preferences for leader traits using PCA scores") mtitle("Competence (PCA)" "Warmth (PCA)" "Dominance (PCA)") modelwidth() sfmt(0) replace compress star(* 0.05 ** 0.01 *** 0.001) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM7b.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM7b.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. ****************************** SOM.8. ANALYSES OF RELATIVE IMPORTANCE USING ALL AVAILABLE RESPONDENTS IN WAVE 1 *************************************************************
. *** SOM 8: Testing the relative importance of leader competence, warmth and dominance in Wave 1 using all available respondents
. reg Comp_scale_1 if Conflict_1 == 1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       528
{txt}{hline 13}{c +}{hline 34}   F(0, 527)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res}  13.078971       527  .024817782   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res}  13.078971       527  .024817782   {txt}Root MSE        =   {res} .15754

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .8824179{col 26}{space 2} .0068559{col 37}{space 1}  128.71{col 46}{space 3}0.000{col 54}{space 4} .8689497{col 67}{space 3} .8958862
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_1 if Conflict_1 == 1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       525
{txt}{hline 13}{c +}{hline 34}   F(0, 524)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 29.2134109       524  .055750784   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 29.2134109       524  .055750784   {txt}Root MSE        =   {res} .23612

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6990476{col 26}{space 2} .0103049{col 37}{space 1}   67.84{col 46}{space 3}0.000{col 54}{space 4} .6788035{col 67}{space 3} .7192917
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_1 if Conflict_1 == 1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       522
{txt}{hline 13}{c +}{hline 34}   F(0, 521)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 35.6961065       521    .0685146   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 35.6961065       521    .0685146   {txt}Root MSE        =   {res} .26175

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5288953{col 26}{space 2} .0114566{col 37}{space 1}   46.17{col 46}{space 3}0.000{col 54}{space 4} .5063884{col 67}{space 3} .5514021
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg Comp_scale_1 if Context== 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       528
{txt}{hline 13}{c +}{hline 34}   F(0, 527)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res}  13.078971       527  .024817782   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res}  13.078971       527  .024817782   {txt}Root MSE        =   {res} .15754

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .8824179{col 26}{space 2} .0068559{col 37}{space 1}  128.71{col 46}{space 3}0.000{col 54}{space 4} .8689497{col 67}{space 3} .8958862
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, level(95)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:528}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .8824179{col 26}{space 2} .0068559{col 37}{space 1}  128.71{col 46}{space 3}0.000{col 54}{space 4} .8689497{col 67}{space 3} .8958862
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(0(.1)1)) ylabel(0(.1)1) recast(scatter) yline(0) ///
> xtitle("Wave 1 (all respondents)") ytitle("Competence Importance") title("Competence") legend(off) plotopts(mcolor(black) msize(small)) ciopts(lcolor(black) lwidth(thin)) scheme(s1mono) name(Comp_war_mean_SOM8, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. reg Warm_scale_1 if Context== 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       525
{txt}{hline 13}{c +}{hline 34}   F(0, 524)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 29.2134109       524  .055750784   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 29.2134109       524  .055750784   {txt}Root MSE        =   {res} .23612

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6990476{col 26}{space 2} .0103049{col 37}{space 1}   67.84{col 46}{space 3}0.000{col 54}{space 4} .6788035{col 67}{space 3} .7192917
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, level(95)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:525}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6990476{col 26}{space 2} .0103049{col 37}{space 1}   67.84{col 46}{space 3}0.000{col 54}{space 4} .6788035{col 67}{space 3} .7192917
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(0(.1)1)) ylabel(0(.1)1) recast(scatter) yline(0) ///
> xtitle("Wave 1 (all respondents)") ytitle("Warmth Importance") title("Warmth") legend(off) plotopts(mcolor(cranberry) msize(small)) ciopts(lcolor(cranberry) lwidth(thin)) scheme(s1mono) name(Warm_war_mean_SOM8, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. reg Domi_scale_1 if Context== 1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       522
{txt}{hline 13}{c +}{hline 34}   F(0, 521)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 35.6961065       521    .0685146   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 35.6961065       521    .0685146   {txt}Root MSE        =   {res} .26175

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5288953{col 26}{space 2} .0114566{col 37}{space 1}   46.17{col 46}{space 3}0.000{col 54}{space 4} .5063884{col 67}{space 3} .5514021
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, level(95)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:522}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5288953{col 26}{space 2} .0114566{col 37}{space 1}   46.17{col 46}{space 3}0.000{col 54}{space 4} .5063884{col 67}{space 3} .5514021
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(0(.1)1)) ylabel(0(.1)1) recast(scatter) yline(0) ///
> xtitle("Wave 1 (all respondents)") ytitle("Dominance Importance") title("Dominance") legend(off) plotopts(mcolor(navy) msize(small)) ciopts(lcolor(navy) lwidth(thin)) scheme(s1mono) name(Domi_war_mean_SOM8, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:}{p_end}
{res}{txt}
{com}. 
. *** Produces Figure SOM.8:
. graph combine Comp_war_mean_SOM8 Warm_war_mean_SOM8 Domi_war_mean_SOM8, scheme(s1mono) cols(3)
{res}{txt}
{com}. graph export FigureSOM8.pdf, replace
{txt}{p 0 4 2}
file {bf}
FigureSOM8.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. *************************************************** SOM.9. ASSESSMENT OF POTENTIAL ATTRITION ********************************************************************
. *** SOM 9: Assessing potential attrition bias
. tab _merge

   {txt}Matching result from {c |}
                  merge {c |}      Freq.     Percent        Cum.
{hline 24}{c +}{hline 35}
        Master only (1) {c |}{res}        270       24.98       24.98
{txt}            Matched (3) {c |}{res}        811       75.02      100.00
{txt}{hline 24}{c +}{hline 35}
                  Total {c |}{res}      1,081      100.00
{txt}
{com}. *[Overall, we reinterviewed 75.02% of Wave 1 sample]
. 
. 
. *Generate dropout variable
. tab _merge, nolab

   {txt}Matching {c |}
result from {c |}
      merge {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        270       24.98       24.98
{txt}          3 {c |}{res}        811       75.02      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,081      100.00
{txt}
{com}. gen dropout = .
{txt}(1,081 missing values generated)

{com}. replace dropout = 1 if _merge == 1
{txt}(270 real changes made)

{com}. replace dropout = 0 if _merge == 3
{txt}(811 real changes made)

{com}. tab dropout 

    {txt}dropout {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        811       75.02       75.02
{txt}          1 {c |}{res}        270       24.98      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,081      100.00
{txt}
{com}. 
. *** ATTRITION TESTS - reported in Table SOM.9
. *Lagged Comp_scale_1 (measured at Wave 1) alone
. eststo clear
{txt}
{com}. 
. eststo: logit dropout Comp_scale_1

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-591.90357}  
Iteration 1:{space 2}Log likelihood = {res:-583.42704}  
Iteration 2:{space 2}Log likelihood = {res:-583.26807}  
Iteration 3:{space 2}Log likelihood = {res:-583.26807}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,057}
{txt}{col 57}{lalign 13:LR chi2({res:1})}{col 70} = {res}{ralign 6:17.27}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-583.26807}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0146}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     dropout{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Comp_scale_1 {c |}{col 14}{res}{space 2}-1.902788{col 26}{space 2} .4557757{col 37}{space 1}   -4.17{col 46}{space 3}0.000{col 54}{space 4}-2.796092{col 67}{space 3}-1.009484
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5738623{col 26}{space 2} .4067128{col 37}{space 1}    1.41{col 46}{space 3}0.158{col 54}{space 4}-.2232802{col 67}{space 3} 1.371005
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. *Lagged Comp_scale_1 together with controls (all measured at Wave 1)
. eststo: logit dropout Comp_scale_1 c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 c.Victimization_1 i.sex c.age i.edu

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-541.11365}  
Iteration 1:{space 2}Log likelihood = {res:-510.61541}  
Iteration 2:{space 2}Log likelihood = {res:-509.86044}  
Iteration 3:{space 2}Log likelihood = {res:-509.86014}  
Iteration 4:{space 2}Log likelihood = {res:-509.86014}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:989}
{txt}{col 57}{lalign 13:LR chi2({res:15})}{col 70} = {res}{ralign 6:62.51}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-509.86014}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0578}

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                             dropout{col 38}{c |} Coefficient{col 50}  Std. err.{col 62}      z{col 70}   P>|z|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}Comp_scale_1 {c |}{col 38}{res}{space 2}-1.081253{col 50}{space 2}  .523149{col 61}{space 1}   -2.07{col 70}{space 3}0.039{col 78}{space 4}-2.106606{col 91}{space 3}-.0558998
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2} .1951682{col 50}{space 2} .4220768{col 61}{space 1}    0.46{col 70}{space 3}0.644{col 78}{space 4}-.6320871{col 91}{space 3} 1.022423
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2}-.5791002{col 50}{space 2} .4015159{col 61}{space 1}   -1.44{col 70}{space 3}0.149{col 78}{space 4}-1.366057{col 91}{space 3} .2078566
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2}-.2379268{col 50}{space 2} .4811734{col 61}{space 1}   -0.49{col 70}{space 3}0.621{col 78}{space 4}-1.181009{col 91}{space 3} .7051557
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2} .7379451{col 50}{space 2} .4306585{col 61}{space 1}    1.71{col 70}{space 3}0.087{col 78}{space 4}-.1061302{col 91}{space 3}  1.58202
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2}-.2327111{col 50}{space 2} .5542189{col 61}{space 1}   -0.42{col 70}{space 3}0.675{col 78}{space 4} -1.31896{col 91}{space 3}  .853538
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2}-.5242592{col 50}{space 2} .2992215{col 61}{space 1}   -1.75{col 70}{space 3}0.080{col 78}{space 4}-1.110722{col 91}{space 3} .0622041
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2} 1.263782{col 50}{space 2} .3609029{col 61}{space 1}    3.50{col 70}{space 3}0.000{col 78}{space 4} .5564249{col 91}{space 3} 1.971138
{txt}{space 21}Victimization_1 {c |}{col 38}{res}{space 2} .0040206{col 50}{space 2} .2888265{col 61}{space 1}    0.01{col 70}{space 3}0.989{col 78}{space 4}-.5620689{col 91}{space 3} .5701102
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.3469176{col 50}{space 2} .1733518{col 61}{space 1}   -2.00{col 70}{space 3}0.045{col 78}{space 4}-.6866808{col 91}{space 3}-.0071544
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0296004{col 50}{space 2} .0093492{col 61}{space 1}   -3.17{col 70}{space 3}0.002{col 78}{space 4}-.0479245{col 91}{space 3}-.0112763
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.2078608{col 50}{space 2} .3113813{col 61}{space 1}   -0.67{col 70}{space 3}0.504{col 78}{space 4}-.8181568{col 91}{space 3} .4024353
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.4573959{col 50}{space 2} .3844207{col 61}{space 1}   -1.19{col 70}{space 3}0.234{col 78}{space 4}-1.210847{col 91}{space 3} .2960547
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.1475759{col 50}{space 2} .3096609{col 61}{space 1}   -0.48{col 70}{space 3}0.634{col 78}{space 4}-.7545001{col 91}{space 3} .4593483
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.1476577{col 50}{space 2} .2821475{col 61}{space 1}   -0.52{col 70}{space 3}0.601{col 78}{space 4}-.7006567{col 91}{space 3} .4053413
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 1.504237{col 50}{space 2} .7309948{col 61}{space 1}    2.06{col 70}{space 3}0.040{col 78}{space 4} .0715133{col 91}{space 3}  2.93696
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. *Lagged Warm_scale_1 (measured at Wave 1) alone
. eststo: logit dropout Warm_scale_1

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-589.10812}  
Iteration 1:{space 2}Log likelihood = {res:-588.83995}  
Iteration 2:{space 2}Log likelihood = {res:-588.83989}  
Iteration 3:{space 2}Log likelihood = {res:-588.83989}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,055}
{txt}{col 57}{lalign 13:LR chi2({res:1})}{col 70} = {res}{ralign 6:0.54}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.4639}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-588.83989}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0005}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     dropout{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Warm_scale_1 {c |}{col 14}{res}{space 2}-.2299565{col 26}{space 2} .3130999{col 37}{space 1}   -0.73{col 46}{space 3}0.463{col 54}{space 4}-.8436211{col 67}{space 3} .3837082
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.9531062{col 26}{space 2} .2342877{col 37}{space 1}   -4.07{col 46}{space 3}0.000{col 54}{space 4}-1.412302{col 67}{space 3}-.4939108
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. *Lagged Warm_scale_1 together with controls (all measured at Wave 1)
. eststo: logit dropout Warm_scale_1  c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 c.Victimization_1 i.sex c.age i.edu

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-539.67065}  
Iteration 1:{space 2}Log likelihood = {res:-511.70449}  
Iteration 2:{space 2}Log likelihood = {res:-511.04227}  
Iteration 3:{space 2}Log likelihood = {res:-511.04201}  
Iteration 4:{space 2}Log likelihood = {res:-511.04201}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:988}
{txt}{col 57}{lalign 13:LR chi2({res:15})}{col 70} = {res}{ralign 6:57.26}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-511.04201}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0530}

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                             dropout{col 38}{c |} Coefficient{col 50}  Std. err.{col 62}      z{col 70}   P>|z|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}Warm_scale_1 {c |}{col 38}{res}{space 2}-.0168971{col 50}{space 2} .3593227{col 61}{space 1}   -0.05{col 70}{space 3}0.962{col 78}{space 4}-.7211566{col 91}{space 3} .6873624
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2} .2644596{col 50}{space 2} .4205809{col 61}{space 1}    0.63{col 70}{space 3}0.529{col 78}{space 4}-.5598638{col 91}{space 3} 1.088783
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2}-.6479237{col 50}{space 2} .3984106{col 61}{space 1}   -1.63{col 70}{space 3}0.104{col 78}{space 4}-1.428794{col 91}{space 3} .1329467
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2}-.2825948{col 50}{space 2} .4794086{col 61}{space 1}   -0.59{col 70}{space 3}0.556{col 78}{space 4}-1.222218{col 91}{space 3} .6570287
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2} .6415661{col 50}{space 2} .4282039{col 61}{space 1}    1.50{col 70}{space 3}0.134{col 78}{space 4} -.197698{col 91}{space 3}  1.48083
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2} -.382378{col 50}{space 2} .5434244{col 61}{space 1}   -0.70{col 70}{space 3}0.482{col 78}{space 4} -1.44747{col 91}{space 3} .6827142
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2}-.5851882{col 50}{space 2} .2989359{col 61}{space 1}   -1.96{col 70}{space 3}0.050{col 78}{space 4}-1.171092{col 91}{space 3} .0007154
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2}  1.24462{col 50}{space 2}  .359386{col 61}{space 1}    3.46{col 70}{space 3}0.001{col 78}{space 4} .5402366{col 91}{space 3} 1.949004
{txt}{space 21}Victimization_1 {c |}{col 38}{res}{space 2} .0564394{col 50}{space 2} .2878653{col 61}{space 1}    0.20{col 70}{space 3}0.845{col 78}{space 4}-.5077663{col 91}{space 3} .6206451
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.3867111{col 50}{space 2} .1722893{col 61}{space 1}   -2.24{col 70}{space 3}0.025{col 78}{space 4} -.724392{col 91}{space 3}-.0490302
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0302791{col 50}{space 2} .0094925{col 61}{space 1}   -3.19{col 70}{space 3}0.001{col 78}{space 4} -.048884{col 91}{space 3}-.0116742
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.2102679{col 50}{space 2} .3110235{col 61}{space 1}   -0.68{col 70}{space 3}0.499{col 78}{space 4}-.8198628{col 91}{space 3}  .399327
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.4735323{col 50}{space 2} .3832492{col 61}{space 1}   -1.24{col 70}{space 3}0.217{col 78}{space 4}-1.224687{col 91}{space 3} .2776224
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.1611681{col 50}{space 2} .3090127{col 61}{space 1}   -0.52{col 70}{space 3}0.602{col 78}{space 4}-.7668218{col 91}{space 3} .4444856
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.1756045{col 50}{space 2} .2810665{col 61}{space 1}   -0.62{col 70}{space 3}0.532{col 78}{space 4}-.7264848{col 91}{space 3} .3752757
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .8776597{col 50}{space 2} .6949718{col 61}{space 1}    1.26{col 70}{space 3}0.207{col 78}{space 4}  -.48446{col 91}{space 3} 2.239779
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. *Lagged Warm_scale_1 (measured at Wave 1) alone
. eststo: logit dropout Domi_scale_1

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-586.85865}  
Iteration 1:{space 2}Log likelihood = {res:-586.69847}  
Iteration 2:{space 2}Log likelihood = {res:-586.69846}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,051}
{txt}{col 57}{lalign 13:LR chi2({res:1})}{col 70} = {res}{ralign 6:0.32}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.5714}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-586.69846}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0003}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     dropout{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Domi_scale_1 {c |}{col 14}{res}{space 2} .1566942{col 26}{space 2} .2769298{col 37}{space 1}    0.57{col 46}{space 3}0.572{col 54}{space 4}-.3860784{col 67}{space 3} .6994667
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.198385{col 26}{space 2} .1601652{col 37}{space 1}   -7.48{col 46}{space 3}0.000{col 54}{space 4}-1.512303{col 67}{space 3}-.8844674
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. *Lagged Domi_scale_1 together with controls (all measured at Wave 1)
. eststo: logit dropout Domi_scale_1  c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 c.Victimization_1 i.sex c.age i.edu

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-540.84352}  
Iteration 1:{space 2}Log likelihood = {res:-511.29193}  
Iteration 2:{space 2}Log likelihood = {res:-510.58109}  
Iteration 3:{space 2}Log likelihood = {res:-510.58075}  
Iteration 4:{space 2}Log likelihood = {res:-510.58075}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:988}
{txt}{col 57}{lalign 13:LR chi2({res:15})}{col 70} = {res}{ralign 6:60.53}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-510.58075}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0560}

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                             dropout{col 38}{c |} Coefficient{col 50}  Std. err.{col 62}      z{col 70}   P>|z|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}Domi_scale_1 {c |}{col 38}{res}{space 2} .4710243{col 50}{space 2} .3096491{col 61}{space 1}    1.52{col 70}{space 3}0.128{col 78}{space 4}-.1358768{col 91}{space 3} 1.077925
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2}  .211228{col 50}{space 2} .4201856{col 61}{space 1}    0.50{col 70}{space 3}0.615{col 78}{space 4}-.6123207{col 91}{space 3} 1.034777
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2}-.7337412{col 50}{space 2} .3996191{col 61}{space 1}   -1.84{col 70}{space 3}0.066{col 78}{space 4} -1.51698{col 91}{space 3} .0494978
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2}-.2845412{col 50}{space 2} .4802835{col 61}{space 1}   -0.59{col 70}{space 3}0.554{col 78}{space 4} -1.22588{col 91}{space 3} .6567972
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2} .6100095{col 50}{space 2} .4303944{col 61}{space 1}    1.42{col 70}{space 3}0.156{col 78}{space 4} -.233548{col 91}{space 3} 1.453567
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2} -.383903{col 50}{space 2} .5430235{col 61}{space 1}   -0.71{col 70}{space 3}0.480{col 78}{space 4} -1.44821{col 91}{space 3} .6804035
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2}-.5560084{col 50}{space 2} .2983569{col 61}{space 1}   -1.86{col 70}{space 3}0.062{col 78}{space 4}-1.140777{col 91}{space 3} .0287603
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2} 1.205746{col 50}{space 2} .3600269{col 61}{space 1}    3.35{col 70}{space 3}0.001{col 78}{space 4} .5001062{col 91}{space 3} 1.911386
{txt}{space 21}Victimization_1 {c |}{col 38}{res}{space 2}  .084251{col 50}{space 2} .2891198{col 61}{space 1}    0.29{col 70}{space 3}0.771{col 78}{space 4}-.4824133{col 91}{space 3} .6509154
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.3453868{col 50}{space 2} .1744334{col 61}{space 1}   -1.98{col 70}{space 3}0.048{col 78}{space 4}-.6872701{col 91}{space 3}-.0035036
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0311069{col 50}{space 2} .0093778{col 61}{space 1}   -3.32{col 70}{space 3}0.001{col 78}{space 4}-.0494871{col 91}{space 3}-.0127267
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.2633194{col 50}{space 2} .3083865{col 61}{space 1}   -0.85{col 70}{space 3}0.393{col 78}{space 4}-.8677458{col 91}{space 3}  .341107
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.5074066{col 50}{space 2} .3810904{col 61}{space 1}   -1.33{col 70}{space 3}0.183{col 78}{space 4} -1.25433{col 91}{space 3} .2395169
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.2089452{col 50}{space 2} .3065091{col 61}{space 1}   -0.68{col 70}{space 3}0.495{col 78}{space 4} -.809692{col 91}{space 3} .3918015
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.2371822{col 50}{space 2} .2779259{col 61}{space 1}   -0.85{col 70}{space 3}0.393{col 78}{space 4}-.7819069{col 91}{space 3} .3075425
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .7609511{col 50}{space 2} .6684905{col 61}{space 1}    1.14{col 70}{space 3}0.255{col 78}{space 4}-.5492661{col 91}{space 3} 2.071168
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}. 
. esttab using TableSOM9.rtf, se(3) b(3) pr2 onecell label nobaselevels title("Table SOM.9: Logit regression estimates of attrition") mtitle("Competence (model 1)" "Competence (model 2)" "Warmth (model 3)" "Warmth (model 4)" "Dominance (model 5)" "Dominance (model 6)") modelwidth(5) sfmt(0) replace compress star(* 0.05 ** 0.01 *** 0.001) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM9.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM9.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. 
. *************************************************** SOM.10. ALTERNATIVE 'RALLY AROUND THE FLAG' EXPLANATION ********************************************************************
. *** SOM 10: Explores traits ratings of President Zelenskyy
. * Do average ratings of Zelensky closely mirror stated trait preferences in ideal leader?
. summ Comp_scale_Zel1 Warm_scale_Zel1 Domi_scale_Zel1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
Comp_scal~l1 {c |}{res}      1,063    .7637713    .2908337          0          1
{txt}Warm_scal~l1 {c |}{res}      1,054    .7145003    .2931831          0          1
{txt}Domi_scal~l1 {c |}{res}      1,052    .4771863    .2808222          0          1
{txt}
{com}. 
. reg Comp_scale_Zel1 if Conflict_1 == 1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       531
{txt}{hline 13}{c +}{hline 34}   F(0, 530)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 46.2469941       530  .087258479   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 46.2469941       530  .087258479   {txt}Root MSE        =   {res}  .2954

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scal~l1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2}  .769094{col 26}{space 2} .0128191{col 37}{space 1}   60.00{col 46}{space 3}0.000{col 54}{space 4} .7439115{col 67}{space 3} .7942764
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_Zel1 if Conflict_1 == 1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       526
{txt}{hline 13}{c +}{hline 34}   F(0, 525)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 47.2798888       525  .090056931   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 47.2798888       525  .090056931   {txt}Root MSE        =   {res} .30009

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scal~l1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .7148289{col 26}{space 2} .0130848{col 37}{space 1}   54.63{col 46}{space 3}0.000{col 54}{space 4}  .689124{col 67}{space 3} .7405338
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_Zel1 if Conflict_1 == 1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       526
{txt}{hline 13}{c +}{hline 34}   F(0, 525)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 45.1691083       525  .086036397   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 45.1691083       525  .086036397   {txt}Root MSE        =   {res} .29332

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scal~l1{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .4786122{col 26}{space 2} .0127893{col 37}{space 1}   37.42{col 46}{space 3}0.000{col 54}{space 4} .4534876{col 67}{space 3} .5037367
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. // Ranking of traits is similar as for ideal leader. But rating of Zelensky's competence is 0.15 scale points lower than for ideal leader.
. 
. * Does context (war vs. peace) affect ratings of Zalenskyy
. reg Comp_scale_Zel1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,063
{txt}{hline 13}{c +}{hline 34}   F(1, 1061)      = {res}     0.36
{txt}       Model {c |} {res} .030058886         1  .030058886   {txt}Prob > F        ={res}    0.5513
{txt}    Residual {c |} {res} 89.7984354     1,061   .08463566   {txt}R-squared       ={res}    0.0003
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0006
{txt}       Total {c |} {res} 89.8284943     1,062   .08458427   {txt}Root MSE        =   {res} .29092

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Comp_scale_Z~1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0106353{col 28}{space 2}  .017846{col 39}{space 1}   -0.60{col 48}{space 3}0.551{col 56}{space 4}-.0456527{col 69}{space 3} .0243821
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}  .769094{col 28}{space 2} .0126249{col 39}{space 1}   60.92{col 48}{space 3}0.000{col 56}{space 4} .7443213{col 69}{space 3} .7938666
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_Zel1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,054
{txt}{hline 13}{c +}{hline 34}   F(1, 1052)      = {res}     0.00
{txt}       Model {c |} {res} .000113364         1  .000113364   {txt}Prob > F        ={res}    0.9711
{txt}    Residual {c |} {res}  90.511882     1,052  .086037911   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0009
{txt}       Total {c |} {res} 90.5119954     1,053  .085956311   {txt}Root MSE        =   {res} .29332

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Warm_scale_Z~1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0006559{col 28}{space 2} .0180699{col 39}{space 1}   -0.04{col 48}{space 3}0.971{col 56}{space 4} -.036113{col 69}{space 3} .0348012
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .7148289{col 28}{space 2} .0127895{col 39}{space 1}   55.89{col 48}{space 3}0.000{col 56}{space 4} .6897332{col 69}{space 3} .7399246
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_Zel1 i.Conflict_1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,052
{txt}{hline 13}{c +}{hline 34}   F(1, 1050)      = {res}     0.03
{txt}       Model {c |} {res}  .00213878         1   .00213878   {txt}Prob > F        ={res}    0.8693
{txt}    Residual {c |} {res} 82.8808866     1,050  .078934178   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0009
{txt}       Total {c |} {res} 82.8830254     1,051  .078861109   {txt}Root MSE        =   {res} .28095

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Domi_scale_Z~1{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Conflict_1 {c |}
Peace, future  {c |}{col 16}{res}{space 2}-.0028517{col 28}{space 2} .0173243{col 39}{space 1}   -0.16{col 48}{space 3}0.869{col 56}{space 4}-.0368458{col 69}{space 3} .0311424
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .4786122{col 28}{space 2} .0122501{col 39}{space 1}   39.07{col 48}{space 3}0.000{col 56}{space 4} .4545747{col 69}{space 3} .5026496
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. // Assigned context does not affect ratings of Zelensky
. 
. 
. 
. *************************************************** SOM.11. INDIVIDUAL VICTIMIZATION OF RUSSIAN ATTACKS ********************************************************************
. 
. *** SOM.11: Exploring if self-reported victimization by Russian attacks affect leader trait preferences
. * Models controlling for changes in identification variables (models 1-3 in SOM.11)
. eststo clear
{txt}
{com}. 
. eststo: reg Comp_scale_diff c.Victimization_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       711
{txt}{hline 13}{c +}{hline 34}   F(4, 706)       = {res}     4.21
{txt}       Model {c |} {res} .283610561         4   .07090264   {txt}Prob > F        ={res}    0.0022
{txt}    Residual {c |} {res} 11.8871864       706  .016837375   {txt}R-squared       ={res}    0.0233
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0178
{txt}       Total {c |} {res}  12.170797       710  .017141968   {txt}Root MSE        =   {res} .12976

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   Comp_scale_diff{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Victimization_diff {c |}{col 20}{res}{space 2}-.0475526{col 32}{space 2} .0189375{col 43}{space 1}   -2.51{col 52}{space 3}0.012{col 60}{space 4}-.0847331{col 73}{space 3} -.010372
{txt}{space 3}ID_Ukraine_diff {c |}{col 20}{res}{space 2} .1021717{col 32}{space 2} .0435817{col 43}{space 1}    2.34{col 52}{space 3}0.019{col 60}{space 4} .0166065{col 73}{space 3} .1877369
{txt}{space 4}ID_Europe_diff {c |}{col 20}{res}{space 2} .0231535{col 32}{space 2}   .02197{col 43}{space 1}    1.05{col 52}{space 3}0.292{col 60}{space 4}-.0199809{col 73}{space 3} .0662879
{txt}{space 4}ID_Russia_diff {c |}{col 20}{res}{space 2}-.0488814{col 32}{space 2} .0311958{col 43}{space 1}   -1.57{col 52}{space 3}0.118{col 60}{space 4}-.1101291{col 73}{space 3} .0123663
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} .0003546{col 32}{space 2}  .005162{col 43}{space 1}    0.07{col 52}{space 3}0.945{col 60}{space 4}-.0097801{col 73}{space 3} .0104893
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. eststo: reg Warm_scale_diff c.Victimization_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       711
{txt}{hline 13}{c +}{hline 34}   F(4, 706)       = {res}     4.63
{txt}       Model {c |} {res} .876387947         4  .219096987   {txt}Prob > F        ={res}    0.0011
{txt}    Residual {c |} {res} 33.4391868       706  .047364287   {txt}R-squared       ={res}    0.0255
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0200
{txt}       Total {c |} {res} 34.3155747       710  .048331795   {txt}Root MSE        =   {res} .21763

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   Warm_scale_diff{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Victimization_diff {c |}{col 20}{res}{space 2} -.032306{col 32}{space 2} .0317622{col 43}{space 1}   -1.02{col 52}{space 3}0.309{col 60}{space 4}-.0946657{col 73}{space 3} .0300537
{txt}{space 3}ID_Ukraine_diff {c |}{col 20}{res}{space 2} .0335988{col 32}{space 2} .0730958{col 43}{space 1}    0.46{col 52}{space 3}0.646{col 60}{space 4}-.1099123{col 73}{space 3} .1771099
{txt}{space 4}ID_Europe_diff {c |}{col 20}{res}{space 2} .1496797{col 32}{space 2} .0368484{col 43}{space 1}    4.06{col 52}{space 3}0.000{col 60}{space 4} .0773341{col 73}{space 3} .2220253
{txt}{space 4}ID_Russia_diff {c |}{col 20}{res}{space 2}-.0635483{col 32}{space 2}  .052322{col 43}{space 1}   -1.21{col 52}{space 3}0.225{col 60}{space 4}-.1662737{col 73}{space 3} .0391771
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}-.0451014{col 32}{space 2} .0086578{col 43}{space 1}   -5.21{col 52}{space 3}0.000{col 60}{space 4}-.0620995{col 73}{space 3}-.0281034
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. eststo: reg Domi_scale_diff c.Victimization_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       711
{txt}{hline 13}{c +}{hline 34}   F(4, 706)       = {res}     0.54
{txt}       Model {c |} {res} .109844408         4  .027461102   {txt}Prob > F        ={res}    0.7092
{txt}    Residual {c |} {res} 36.1560557       706  .051212543   {txt}R-squared       ={res}    0.0030
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0026
{txt}       Total {c |} {res} 36.2659001       710  .051078733   {txt}Root MSE        =   {res}  .2263

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   Domi_scale_diff{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Victimization_diff {c |}{col 20}{res}{space 2}-.0260402{col 32}{space 2} .0330273{col 43}{space 1}   -0.79{col 52}{space 3}0.431{col 60}{space 4}-.0908838{col 73}{space 3} .0388033
{txt}{space 3}ID_Ukraine_diff {c |}{col 20}{res}{space 2} .0590337{col 32}{space 2} .0760072{col 43}{space 1}    0.78{col 52}{space 3}0.438{col 60}{space 4}-.0901936{col 73}{space 3} .2082609
{txt}{space 4}ID_Europe_diff {c |}{col 20}{res}{space 2}-.0209786{col 32}{space 2} .0383161{col 43}{space 1}   -0.55{col 52}{space 3}0.584{col 60}{space 4}-.0962058{col 73}{space 3} .0542486
{txt}{space 4}ID_Russia_diff {c |}{col 20}{res}{space 2} .0524634{col 32}{space 2} .0544061{col 43}{space 1}    0.96{col 52}{space 3}0.335{col 60}{space 4}-.0543537{col 73}{space 3} .1592804
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} .0229764{col 32}{space 2} .0090026{col 43}{space 1}    2.55{col 52}{space 3}0.011{col 60}{space 4} .0053013{col 73}{space 3} .0406514
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. 
. * Models also controlling for changes in emotional reactions (models 4-6 in SOM.11)
. eststo: reg Comp_scale_diff c.Victimization_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       706
{txt}{hline 13}{c +}{hline 34}   F(8, 697)       = {res}     4.24
{txt}       Model {c |} {res} .563055925         8  .070381991   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 11.5636544       697  .016590609   {txt}R-squared       ={res}    0.0464
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0355
{txt}       Total {c |} {res} 12.1267104       705  .017201008   {txt}Root MSE        =   {res}  .1288

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   Comp_scale_diff{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Victimization_diff {c |}{col 20}{res}{space 2}-.0506781{col 32}{space 2} .0189094{col 43}{space 1}   -2.68{col 52}{space 3}0.008{col 60}{space 4}-.0878044{col 73}{space 3}-.0135519
{txt}{space 5}fearfull_diff {c |}{col 20}{res}{space 2}-.0563143{col 32}{space 2} .0266272{col 43}{space 1}   -2.11{col 52}{space 3}0.035{col 60}{space 4}-.1085934{col 73}{space 3}-.0040352
{txt}{space 3}aggressive_diff {c |}{col 20}{res}{space 2} .0679219{col 32}{space 2} .0261436{col 43}{space 1}    2.60{col 52}{space 3}0.010{col 60}{space 4} .0165922{col 73}{space 3} .1192516
{txt}{space 6}sadness_diff {c |}{col 20}{res}{space 2} .0437024{col 32}{space 2} .0271144{col 43}{space 1}    1.61{col 52}{space 3}0.107{col 60}{space 4}-.0095332{col 73}{space 3} .0969381
{txt}{space 5}selfconf_diff {c |}{col 20}{res}{space 2} .0396815{col 32}{space 2} .0277011{col 43}{space 1}    1.43{col 52}{space 3}0.152{col 60}{space 4}-.0147061{col 73}{space 3} .0940691
{txt}{space 3}ID_Ukraine_diff {c |}{col 20}{res}{space 2}  .086504{col 32}{space 2} .0438741{col 43}{space 1}    1.97{col 52}{space 3}0.049{col 60}{space 4} .0003627{col 73}{space 3} .1726453
{txt}{space 4}ID_Europe_diff {c |}{col 20}{res}{space 2} .0156595{col 32}{space 2} .0219826{col 43}{space 1}    0.71{col 52}{space 3}0.476{col 60}{space 4}-.0275005{col 73}{space 3} .0588196
{txt}{space 4}ID_Russia_diff {c |}{col 20}{res}{space 2}-.0424028{col 32}{space 2} .0312252{col 43}{space 1}   -1.36{col 52}{space 3}0.175{col 60}{space 4}-.1037095{col 73}{space 3}  .018904
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}-.0035746{col 32}{space 2} .0053924{col 43}{space 1}   -0.66{col 52}{space 3}0.508{col 60}{space 4} -.014162{col 73}{space 3} .0070127
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. eststo: reg Warm_scale_diff c.Victimization_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       706
{txt}{hline 13}{c +}{hline 34}   F(8, 697)       = {res}     3.83
{txt}       Model {c |} {res} 1.44162484         8  .180203105   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 32.8261573       697  .047096352   {txt}R-squared       ={res}    0.0421
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0311
{txt}       Total {c |} {res} 34.2677822       705  .048606783   {txt}Root MSE        =   {res} .21702

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   Warm_scale_diff{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Victimization_diff {c |}{col 20}{res}{space 2}-.0334488{col 32}{space 2} .0318596{col 43}{space 1}   -1.05{col 52}{space 3}0.294{col 60}{space 4}-.0960011{col 73}{space 3} .0291036
{txt}{space 5}fearfull_diff {c |}{col 20}{res}{space 2} -.077192{col 32}{space 2} .0448629{col 43}{space 1}   -1.72{col 52}{space 3}0.086{col 60}{space 4}-.1652747{col 73}{space 3} .0108907
{txt}{space 3}aggressive_diff {c |}{col 20}{res}{space 2} .0904302{col 32}{space 2} .0440482{col 43}{space 1}    2.05{col 52}{space 3}0.040{col 60}{space 4} .0039471{col 73}{space 3} .1769133
{txt}{space 6}sadness_diff {c |}{col 20}{res}{space 2} .1093213{col 32}{space 2} .0456838{col 43}{space 1}    2.39{col 52}{space 3}0.017{col 60}{space 4}  .019627{col 73}{space 3} .1990156
{txt}{space 5}selfconf_diff {c |}{col 20}{res}{space 2} -.048069{col 32}{space 2} .0466723{col 43}{space 1}   -1.03{col 52}{space 3}0.303{col 60}{space 4}-.1397042{col 73}{space 3} .0435661
{txt}{space 3}ID_Ukraine_diff {c |}{col 20}{res}{space 2} .0310155{col 32}{space 2} .0739215{col 43}{space 1}    0.42{col 52}{space 3}0.675{col 60}{space 4}-.1141201{col 73}{space 3} .1761511
{txt}{space 4}ID_Europe_diff {c |}{col 20}{res}{space 2} .1389846{col 32}{space 2} .0370375{col 43}{space 1}    3.75{col 52}{space 3}0.000{col 60}{space 4} .0662662{col 73}{space 3} .2117029
{txt}{space 4}ID_Russia_diff {c |}{col 20}{res}{space 2}-.0566594{col 32}{space 2} .0526099{col 43}{space 1}   -1.08{col 52}{space 3}0.282{col 60}{space 4}-.1599523{col 73}{space 3} .0466336
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} -.050552{col 32}{space 2} .0090855{col 43}{space 1}   -5.56{col 52}{space 3}0.000{col 60}{space 4}-.0683902{col 73}{space 3}-.0327138
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{com}. eststo: reg Domi_scale_diff c.Victimization_diff c.fearfull_diff c.aggressive_diff c.sadness_diff c.selfconf_diff c.ID_Ukraine_diff c.ID_Europe_diff c.ID_Russia_diff if include==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       706
{txt}{hline 13}{c +}{hline 34}   F(8, 697)       = {res}     1.02
{txt}       Model {c |} {res} .410676465         8  .051334558   {txt}Prob > F        ={res}    0.4212
{txt}    Residual {c |} {res} 35.1710921       697  .050460677   {txt}R-squared       ={res}    0.0115
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0002
{txt}       Total {c |} {res} 35.5817685       705  .050470594   {txt}Root MSE        =   {res} .22463

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   Domi_scale_diff{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Victimization_diff {c |}{col 20}{res}{space 2}-.0334112{col 32}{space 2} .0329779{col 43}{space 1}   -1.01{col 52}{space 3}0.311{col 60}{space 4}-.0981592{col 73}{space 3} .0313368
{txt}{space 5}fearfull_diff {c |}{col 20}{res}{space 2} .0123753{col 32}{space 2} .0464377{col 43}{space 1}    0.27{col 52}{space 3}0.790{col 60}{space 4}-.0787993{col 73}{space 3} .1035498
{txt}{space 3}aggressive_diff {c |}{col 20}{res}{space 2}  .097824{col 32}{space 2} .0455944{col 43}{space 1}    2.15{col 52}{space 3}0.032{col 60}{space 4} .0083052{col 73}{space 3} .1873428
{txt}{space 6}sadness_diff {c |}{col 20}{res}{space 2} .0277847{col 32}{space 2} .0472873{col 43}{space 1}    0.59{col 52}{space 3}0.557{col 60}{space 4}-.0650581{col 73}{space 3} .1206274
{txt}{space 5}selfconf_diff {c |}{col 20}{res}{space 2} .0072708{col 32}{space 2} .0483106{col 43}{space 1}    0.15{col 52}{space 3}0.880{col 60}{space 4}-.0875809{col 73}{space 3} .1021225
{txt}{space 3}ID_Ukraine_diff {c |}{col 20}{res}{space 2} .0475118{col 32}{space 2} .0765163{col 43}{space 1}    0.62{col 52}{space 3}0.535{col 60}{space 4}-.1027182{col 73}{space 3} .1977419
{txt}{space 4}ID_Europe_diff {c |}{col 20}{res}{space 2}-.0275922{col 32}{space 2} .0383375{col 43}{space 1}   -0.72{col 52}{space 3}0.472{col 60}{space 4}-.1028631{col 73}{space 3} .0476786
{txt}{space 4}ID_Russia_diff {c |}{col 20}{res}{space 2} .0520365{col 32}{space 2} .0544566{col 43}{space 1}    0.96{col 52}{space 3}0.340{col 60}{space 4}-.0548822{col 73}{space 3} .1589552
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} .0233883{col 32}{space 2} .0094044{col 43}{space 1}    2.49{col 52}{space 3}0.013{col 60}{space 4} .0049239{col 73}{space 3} .0418526
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{com}. 
. esttab using TableSOM11.rtf, se(3) b(3) ar2 onecell label nobaselevels title("Table SOM.11: OLS regression estimates of change (from Wave 1 to Wave 2) in importance of leader traits (competence (Models 1 and 4), warmth (Models 2 and 5), and dominance (Models 3 and 6)) as a function of change in self-reported victimization by the Russian attacks.") mtitle("Competence (model 1)" "Warmth (model 2)" "Dominance (model 3)" "Competence (model 4)" "Warmth (model 5)" "Dominance (model 6)") modelwidth(5) sfmt(0) replace compress star(* 0.05 ** 0.01 *** 0.001) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM11.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM11.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. *************************************************** SOM.12. OBLAST-LEVEL ANALYSES OF RUSSIAN ATTACKS ********************************************************************
. 
. *** SOM.12: Exploring if oblast-level differences in attack intensity relates to trait preferences in leaders
. ** Adding VIINA events to the dataset
. * Wave 1: N of events per oblast
. tab w1_q5

             {txt}5.1 Oblast {c |}      Freq.     Percent        Cum.
{hline 24}{c +}{hline 35}
              Vinnytsia {c |}{res}         25        2.31        2.31
{txt}                  Volyn {c |}{res}         10        0.93        3.24
{txt}         Dnipropetrovsk {c |}{res}        122       11.29       14.52
{txt}                Donetsk {c |}{res}         33        3.05       17.58
{txt}               Zhytomyr {c |}{res}         27        2.50       20.07
{txt}        Transcarpathian {c |}{res}         15        1.39       21.46
{txt}           Zaporizhzhia {c |}{res}         62        5.74       27.20
{txt}        Ivano-Frankivsk {c |}{res}         28        2.59       29.79
{txt}                   Kyiv {c |}{res}         37        3.42       33.21
{txt}                   Kyiv {c |}{res}        195       18.04       51.25
{txt}             Kirovohrad {c |}{res}         21        1.94       53.19
{txt}                Luhansk {c |}{res}         10        0.93       54.12
{txt}                   Lviv {c |}{res}         62        5.74       59.85
{txt}               Mykolaiv {c |}{res}         39        3.61       63.46
{txt}                  Odesa {c |}{res}         71        6.57       70.03
{txt}                Poltava {c |}{res}         40        3.70       73.73
{txt}                  Rivne {c |}{res}         21        1.94       75.67
{txt}                   Sumy {c |}{res}         25        2.31       77.98
{txt}               Ternopil {c |}{res}         18        1.67       79.65
{txt}                Kharkiv {c |}{res}         94        8.70       88.34
{txt}                Kherson {c |}{res}         21        1.94       90.29
{txt}            Khmelnytsky {c |}{res}         28        2.59       92.88
{txt}               Cherkasy {c |}{res}         37        3.42       96.30
{txt}             Chernivtsi {c |}{res}         17        1.57       97.87
{txt}              Chernihiv {c |}{res}         23        2.13      100.00
{txt}{hline 24}{c +}{hline 35}
                  Total {c |}{res}      1,081      100.00
{txt}
{com}. tab w1_q5, nolab

 {txt}5.1 Oblast {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}         25        2.31        2.31
{txt}          3 {c |}{res}         10        0.93        3.24
{txt}          4 {c |}{res}        122       11.29       14.52
{txt}          5 {c |}{res}         33        3.05       17.58
{txt}          6 {c |}{res}         27        2.50       20.07
{txt}          7 {c |}{res}         15        1.39       21.46
{txt}          8 {c |}{res}         62        5.74       27.20
{txt}          9 {c |}{res}         28        2.59       29.79
{txt}         10 {c |}{res}         37        3.42       33.21
{txt}         11 {c |}{res}        195       18.04       51.25
{txt}         12 {c |}{res}         21        1.94       53.19
{txt}         13 {c |}{res}         10        0.93       54.12
{txt}         14 {c |}{res}         62        5.74       59.85
{txt}         15 {c |}{res}         39        3.61       63.46
{txt}         16 {c |}{res}         71        6.57       70.03
{txt}         17 {c |}{res}         40        3.70       73.73
{txt}         18 {c |}{res}         21        1.94       75.67
{txt}         19 {c |}{res}         25        2.31       77.98
{txt}         20 {c |}{res}         18        1.67       79.65
{txt}         21 {c |}{res}         94        8.70       88.34
{txt}         22 {c |}{res}         21        1.94       90.29
{txt}         23 {c |}{res}         28        2.59       92.88
{txt}         24 {c |}{res}         37        3.42       96.30
{txt}         25 {c |}{res}         17        1.57       97.87
{txt}         26 {c |}{res}         23        2.13      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,081      100.00
{txt}
{com}. 
. ** N of VIINA events (all types) in the 2-week period before Wave 1
. * 2 Vinnytsya 279
. * 3 Volyn       120
. * 4 Dnipropetrovsk 279
. * 5 Donetsk 2101
. * 6 Zhytomyr 454
. * 7 Transcarpathian 76
. * 8 Zaporizhzhia 1280
. * 9 Ivano-Frankivsk 51
. * 10 Kyiv 1995
. * 11 Kyiv city 3704
. * 12 Kirovohrad 40
. * 13 Luhansk 720
. * 14 Lviv 393
. * 15 Mykolayiv 805
. * 16 Odessa 695
. * 17 Poltava 126
. * 18 Rivne 90
. * 19 Sumy 1091
. * 20 Ternopil 57
. * 21 Kharkiv 2436
. * 22 Kherson 938
. * 23 Khmelnytsky 121
. * 24 Cherkasy 139
. * 25 Chernivtsi 29
. * 26 Chernihiv 817
. * Crimea 387 [no respondents reached in Crimea]
. * Sevastopol 21 [no respondents reached in Sevastapol]
. 
. * Enters VIINA events/observations for Wave 1 to dataset
. gen w1_VIINA_events = .
{txt}(1,081 missing values generated)

{com}. replace w1_VIINA_events = 279 if w1_q5 == 2
{txt}(25 real changes made)

{com}. replace w1_VIINA_events = 120 if w1_q5 == 3
{txt}(10 real changes made)

{com}. replace w1_VIINA_events = 279 if w1_q5 == 4
{txt}(122 real changes made)

{com}. replace w1_VIINA_events = 2101 if w1_q5 == 5
{txt}(33 real changes made)

{com}. replace w1_VIINA_events = 454 if w1_q5 == 6
{txt}(27 real changes made)

{com}. replace w1_VIINA_events = 76 if w1_q5 == 7
{txt}(15 real changes made)

{com}. replace w1_VIINA_events = 1280 if w1_q5 == 8
{txt}(62 real changes made)

{com}. replace w1_VIINA_events = 51 if w1_q5 == 9
{txt}(28 real changes made)

{com}. replace w1_VIINA_events = 1995 if w1_q5 == 10
{txt}(37 real changes made)

{com}. replace w1_VIINA_events = 3704 if w1_q5 == 11
{txt}(195 real changes made)

{com}. replace w1_VIINA_events = 40 if w1_q5 == 12
{txt}(21 real changes made)

{com}. replace w1_VIINA_events = 720 if w1_q5 == 13
{txt}(10 real changes made)

{com}. replace w1_VIINA_events = 393 if w1_q5 == 14
{txt}(62 real changes made)

{com}. replace w1_VIINA_events = 805 if w1_q5 == 15
{txt}(39 real changes made)

{com}. replace w1_VIINA_events = 695 if w1_q5 == 16
{txt}(71 real changes made)

{com}. replace w1_VIINA_events = 126 if w1_q5 == 17
{txt}(40 real changes made)

{com}. replace w1_VIINA_events = 90 if w1_q5 == 18
{txt}(21 real changes made)

{com}. replace w1_VIINA_events = 1091 if w1_q5 == 19
{txt}(25 real changes made)

{com}. replace w1_VIINA_events = 57 if w1_q5 == 20
{txt}(18 real changes made)

{com}. replace w1_VIINA_events = 2436 if w1_q5 == 21
{txt}(94 real changes made)

{com}. replace w1_VIINA_events = 938 if w1_q5 == 22
{txt}(21 real changes made)

{com}. replace w1_VIINA_events = 121 if w1_q5 == 23
{txt}(28 real changes made)

{com}. replace w1_VIINA_events = 139 if w1_q5 == 24
{txt}(37 real changes made)

{com}. replace w1_VIINA_events = 29 if w1_q5 == 25
{txt}(17 real changes made)

{com}. replace w1_VIINA_events = 817 if w1_q5 == 26
{txt}(23 real changes made)

{com}. 
. ** Normalizes VIINA variable for Wave 1
. summ w1_VIINA_events

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
w1_VIINA_e~s {c |}{res}      1,081    1319.797     1327.05         29       3704
{txt}
{com}. gen VIINA_Oblast_attacks_W1 = (w1_VIINA_events - r(min)) / (r(max) - r(min)) 
{txt}
{com}. summ VIINA_Oblast_attacks_W1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
VIINA_Obla~1 {c |}{res}      1,081    .3512374    .3611019          0          1
{txt}
{com}. 
. ** Creates log-transformed version of the normalized VIINA variable for Wave 1
. gen ln_VIINA_W1_norm = ln(VIINA_Oblast_attacks_W1)
{txt}(17 missing values generated)

{com}. 
. 
. 
. ** N of VIINA events (all types) in the 2-week period before Wave 2
. * 2 Vinnytsya 72
. * 3 Volyn       150
. * 4 Dnipropetrovsk 349
. * 5 Donetsk 2274
. * 6 Zhytomyr 180
. * 7 Transcarpathian 117
. * 8 Zaporizhzhia 763
. * 9 Ivano-Frankivsk 37
. * 10 Kyiv 1435
. * 11 Kyiv city 1995
. * 12 Kirovohrad 20
. * 13 Luhansk 840
. * 14 Lviv 429
. * 15 Mykolayiv 415
. * 16 Odessa 486
. * 17 Poltava 86
. * 18 Rivne 120
. * 19 Sumy 465
. * 20 Ternopil 27
. * 21 Kharkiv 1258
. * 22 Kherson 709
. * 23 Khmelnytsky 82
. * 24 Cherkasy 32
. * 25 Chernivtsi 43
. * 26 Chernihiv 680
. * Crimea 335 [no respondents reached in Crimea]
. * Sevastopol 41 [no respondents reached in Sevastapol]
. 
. * Enters VIINA events/observations for Wave 2 to dataset
. gen w2_VIINA_events = .
{txt}(1,081 missing values generated)

{com}. replace w2_VIINA_events = 72 if w2_q4 == 2
{txt}(22 real changes made)

{com}. replace w2_VIINA_events = 150 if w2_q4 == 3
{txt}(6 real changes made)

{com}. replace w2_VIINA_events = 349 if w2_q4 == 4
{txt}(95 real changes made)

{com}. replace w2_VIINA_events = 2274 if w2_q4 == 5
{txt}(19 real changes made)

{com}. replace w2_VIINA_events = 180 if w2_q4 == 6
{txt}(20 real changes made)

{com}. replace w2_VIINA_events = 117 if w2_q4 == 7
{txt}(10 real changes made)

{com}. replace w2_VIINA_events = 763 if w2_q4 == 8
{txt}(48 real changes made)

{com}. replace w2_VIINA_events = 37 if w2_q4 == 9
{txt}(32 real changes made)

{com}. replace w2_VIINA_events = 1435 if w2_q4 == 10
{txt}(31 real changes made)

{com}. replace w2_VIINA_events = 1995 if w2_q4 == 11
{txt}(110 real changes made)

{com}. replace w2_VIINA_events = 20 if w2_q4 == 12
{txt}(18 real changes made)

{com}. replace w2_VIINA_events = 840 if w2_q4 == 13
{txt}(5 real changes made)

{com}. replace w2_VIINA_events = 429 if w2_q4 == 14
{txt}(60 real changes made)

{com}. replace w2_VIINA_events = 415 if w2_q4 == 15
{txt}(22 real changes made)

{com}. replace w2_VIINA_events = 486 if w2_q4 == 16
{txt}(50 real changes made)

{com}. replace w2_VIINA_events = 86 if w2_q4 == 17
{txt}(42 real changes made)

{com}. replace w2_VIINA_events = 120 if w2_q4 == 18
{txt}(20 real changes made)

{com}. replace w2_VIINA_events = 465 if w2_q4 == 19
{txt}(21 real changes made)

{com}. replace w2_VIINA_events = 27 if w2_q4 == 20
{txt}(17 real changes made)

{com}. replace w2_VIINA_events = 1258 if w2_q4 == 21
{txt}(36 real changes made)

{com}. replace w2_VIINA_events = 709 if w2_q4 == 22
{txt}(18 real changes made)

{com}. replace w2_VIINA_events = 82 if w2_q4 == 23
{txt}(16 real changes made)

{com}. replace w2_VIINA_events = 32 if w2_q4 == 24
{txt}(32 real changes made)

{com}. replace w2_VIINA_events = 43 if w2_q4 == 25
{txt}(13 real changes made)

{com}. replace w2_VIINA_events = 680 if w2_q4 == 26
{txt}(20 real changes made)

{com}. 
. ** Normalizes VIINA variable for Wave 1
. summ w2_VIINA_events

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
w2_VIINA_e~s {c |}{res}        783    689.4879    696.1808         20       2274
{txt}
{com}. gen VIINA_Oblast_attacks_W2 = (w2_VIINA_events - r(min)) / (r(max) - r(min)) 
{txt}(298 missing values generated)

{com}. summ VIINA_Oblast_attacks_W2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
VIINA_Obla~2 {c |}{res}        783    .2970221    .3088646          0          1
{txt}
{com}. 
. ** Creates log-transformed version of the normalized VIINA variable for Wave 1
. gen ln_VIINA_W2_norm = ln(VIINA_Oblast_attacks_W2)
{txt}(316 missing values generated)

{com}. 
. 
. 
. 
. ******* Results reported in SOM.12: Predictions of leader trait preferences from VIINA events (standard errors clustered at oblast-level) **********
. 
. **** Wave 1 - Produces Table SOM.12a
. eststo clear
{txt}
{com}. 
. eststo: reg Comp_scale_1 c.VIINA_Oblast_attacks_W1 c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 i.sex c.age i.education, cluster(w1_q5)

{txt}Linear regression                               Number of obs     = {res}     1,012
                                                {txt}F(14, 24)         =  {res}    59.33
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1296
                                                {txt}Root MSE          =    {res} .13651

{txt}{ralign 102:(Std. err. adjusted for {res:25} clusters in {res:w1_q5})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Comp_scale_1{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}VIINA_Oblast_attacks_W1 {c |}{col 38}{res}{space 2}-.0096757{col 50}{space 2} .0081714{col 61}{space 1}   -1.18{col 70}{space 3}0.248{col 78}{space 4}-.0265407{col 91}{space 3} .0071893
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2}-.0425799{col 50}{space 2} .0282878{col 61}{space 1}   -1.51{col 70}{space 3}0.145{col 78}{space 4}-.1009631{col 91}{space 3} .0158032
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2}  .079017{col 50}{space 2} .0246296{col 61}{space 1}    3.21{col 70}{space 3}0.004{col 78}{space 4}  .028184{col 91}{space 3} .1298499
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2} .0551254{col 50}{space 2} .0269799{col 61}{space 1}    2.04{col 70}{space 3}0.052{col 78}{space 4}-.0005583{col 91}{space 3} .1108091
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2} .0697216{col 50}{space 2} .0291557{col 61}{space 1}    2.39{col 70}{space 3}0.025{col 78}{space 4} .0095472{col 91}{space 3}  .129896
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2}  .131721{col 50}{space 2} .0479649{col 61}{space 1}    2.75{col 70}{space 3}0.011{col 78}{space 4} .0327264{col 91}{space 3} .2307156
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2} .0437404{col 50}{space 2} .0244811{col 61}{space 1}    1.79{col 70}{space 3}0.087{col 78}{space 4}-.0067862{col 91}{space 3}  .094267
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2}-.0061298{col 50}{space 2} .0325483{col 61}{space 1}   -0.19{col 70}{space 3}0.852{col 78}{space 4}-.0733062{col 91}{space 3} .0610466
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0224803{col 50}{space 2} .0081674{col 61}{space 1}    2.75{col 70}{space 3}0.011{col 78}{space 4} .0056236{col 91}{space 3} .0393371
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0002181{col 50}{space 2} .0005233{col 61}{space 1}   -0.42{col 70}{space 3}0.681{col 78}{space 4}-.0012982{col 91}{space 3}  .000862
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}  .027332{col 50}{space 2} .0200714{col 61}{space 1}    1.36{col 70}{space 3}0.186{col 78}{space 4}-.0140933{col 91}{space 3} .0687572
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2} .0418732{col 50}{space 2} .0203612{col 61}{space 1}    2.06{col 70}{space 3}0.051{col 78}{space 4}-.0001503{col 91}{space 3} .0838968
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2} .0420831{col 50}{space 2} .0230371{col 61}{space 1}    1.83{col 70}{space 3}0.080{col 78}{space 4}-.0054631{col 91}{space 3} .0896292
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2} .0562099{col 50}{space 2} .0158051{col 61}{space 1}    3.56{col 70}{space 3}0.002{col 78}{space 4} .0235898{col 91}{space 3}   .08883
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5962104{col 50}{space 2} .0614431{col 61}{space 1}    9.70{col 70}{space 3}0.000{col 78}{space 4}  .469398{col 91}{space 3} .7230228
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. eststo: reg Warm_scale_1 c.VIINA_Oblast_attacks_W1 c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 i.sex c.age i.education, cluster(w1_q5)

{txt}Linear regression                               Number of obs     = {res}     1,010
                                                {txt}F(14, 24)         =  {res}    38.15
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1123
                                                {txt}Root MSE          =    {res} .21519

{txt}{ralign 102:(Std. err. adjusted for {res:25} clusters in {res:w1_q5})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Warm_scale_1{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}VIINA_Oblast_attacks_W1 {c |}{col 38}{res}{space 2}-.0184231{col 50}{space 2} .0128877{col 61}{space 1}   -1.43{col 70}{space 3}0.166{col 78}{space 4} -.045022{col 91}{space 3} .0081758
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2} .0990457{col 50}{space 2} .0441623{col 61}{space 1}    2.24{col 70}{space 3}0.034{col 78}{space 4} .0078993{col 91}{space 3} .1901921
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2}-.0043112{col 50}{space 2} .0432222{col 61}{space 1}   -0.10{col 70}{space 3}0.921{col 78}{space 4}-.0935176{col 91}{space 3} .0848951
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2} .0991296{col 50}{space 2} .0651628{col 61}{space 1}    1.52{col 70}{space 3}0.141{col 78}{space 4}-.0353598{col 91}{space 3}  .233619
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2} .1052093{col 50}{space 2} .0368444{col 61}{space 1}    2.86{col 70}{space 3}0.009{col 78}{space 4} .0291663{col 91}{space 3} .1812523
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2} .1046643{col 50}{space 2} .0707795{col 61}{space 1}    1.48{col 70}{space 3}0.152{col 78}{space 4}-.0414174{col 91}{space 3} .2507461
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2} .0646321{col 50}{space 2} .0319918{col 61}{space 1}    2.02{col 70}{space 3}0.055{col 78}{space 4}-.0013956{col 91}{space 3} .1306598
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2}  .005405{col 50}{space 2}  .033667{col 61}{space 1}    0.16{col 70}{space 3}0.874{col 78}{space 4}-.0640803{col 91}{space 3} .0748904
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0171565{col 50}{space 2} .0152081{col 61}{space 1}    1.13{col 70}{space 3}0.270{col 78}{space 4}-.0142314{col 91}{space 3} .0485445
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0048571{col 50}{space 2}  .000626{col 61}{space 1}   -7.76{col 70}{space 3}0.000{col 78}{space 4}-.0061491{col 91}{space 3}-.0035651
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0027496{col 50}{space 2}  .025919{col 61}{space 1}   -0.11{col 70}{space 3}0.916{col 78}{space 4}-.0562438{col 91}{space 3} .0507445
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2} -.018459{col 50}{space 2} .0332708{col 61}{space 1}   -0.55{col 70}{space 3}0.584{col 78}{space 4}-.0871266{col 91}{space 3} .0502087
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.0574968{col 50}{space 2} .0311708{col 61}{space 1}   -1.84{col 70}{space 3}0.077{col 78}{space 4}-.1218302{col 91}{space 3} .0068367
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.0532765{col 50}{space 2} .0254322{col 61}{space 1}   -2.09{col 70}{space 3}0.047{col 78}{space 4}-.1057659{col 91}{space 3} -.000787
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6220129{col 50}{space 2} .0607372{col 61}{space 1}   10.24{col 70}{space 3}0.000{col 78}{space 4} .4966573{col 91}{space 3} .7473684
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. eststo: reg Domi_scale_1 c.VIINA_Oblast_attacks_W1 c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 i.sex c.age i.education, cluster(w1_q5)

{txt}Linear regression                               Number of obs     = {res}     1,009
                                                {txt}F(14, 24)         =  {res}    16.84
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0617
                                                {txt}Root MSE          =    {res} .25267

{txt}{ralign 102:(Std. err. adjusted for {res:25} clusters in {res:w1_q5})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Domi_scale_1{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}VIINA_Oblast_attacks_W1 {c |}{col 38}{res}{space 2}-.0402314{col 50}{space 2} .0276414{col 61}{space 1}   -1.46{col 70}{space 3}0.158{col 78}{space 4}-.0972804{col 91}{space 3} .0168176
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2} .0562479{col 50}{space 2} .0502032{col 61}{space 1}    1.12{col 70}{space 3}0.274{col 78}{space 4}-.0473664{col 91}{space 3} .1598623
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2} .0678551{col 50}{space 2} .0365506{col 61}{space 1}    1.86{col 70}{space 3}0.076{col 78}{space 4}-.0075817{col 91}{space 3} .1432918
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2} .0546184{col 50}{space 2} .0648623{col 61}{space 1}    0.84{col 70}{space 3}0.408{col 78}{space 4}-.0792509{col 91}{space 3} .1884876
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2} .1348081{col 50}{space 2} .0453993{col 61}{space 1}    2.97{col 70}{space 3}0.007{col 78}{space 4} .0411085{col 91}{space 3} .2285077
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2} .0077534{col 50}{space 2} .0442257{col 61}{space 1}    0.18{col 70}{space 3}0.862{col 78}{space 4} -.083524{col 91}{space 3} .0990309
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2}-.0361663{col 50}{space 2} .0313045{col 61}{space 1}   -1.16{col 70}{space 3}0.259{col 78}{space 4}-.1007757{col 91}{space 3} .0284431
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2} .0686272{col 50}{space 2} .0318387{col 61}{space 1}    2.16{col 70}{space 3}0.041{col 78}{space 4} .0029154{col 91}{space 3}  .134339
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0940792{col 50}{space 2} .0155864{col 61}{space 1}   -6.04{col 70}{space 3}0.000{col 78}{space 4} -.126248{col 91}{space 3}-.0619103
{txt}{space 33}age {c |}{col 38}{res}{space 2} .0019488{col 50}{space 2} .0011702{col 61}{space 1}    1.67{col 70}{space 3}0.109{col 78}{space 4}-.0004663{col 91}{space 3} .0043639
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2} .0258005{col 50}{space 2}  .035342{col 61}{space 1}    0.73{col 70}{space 3}0.472{col 78}{space 4}-.0471418{col 91}{space 3} .0987428
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.0220183{col 50}{space 2} .0339259{col 61}{space 1}   -0.65{col 70}{space 3}0.522{col 78}{space 4}-.0920379{col 91}{space 3} .0480013
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2} .0373062{col 50}{space 2} .0333662{col 61}{space 1}    1.12{col 70}{space 3}0.275{col 78}{space 4}-.0315583{col 91}{space 3} .1061708
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2} .0460874{col 50}{space 2} .0253667{col 61}{space 1}    1.82{col 70}{space 3}0.082{col 78}{space 4}-.0062669{col 91}{space 3} .0984418
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}   .30615{col 50}{space 2} .0759566{col 61}{space 1}    4.03{col 70}{space 3}0.000{col 78}{space 4} .1493832{col 91}{space 3} .4629167
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. 
. esttab using TableSOM12a.rtf, se(3) b(3) ar2 onecell label nobaselevels title("Table SOM.12.a: OLS regression estimates of respondent ratings of importance of leader traits (competence, warmth, and dominance) as a function of oblast-level incidences of Russian attacks in two weeks leading up to Wave 1.") mtitle("Competence (model 1)" "Warmth (model 2)" "Dominance (model 3)") modelwidth() sfmt(0) replace compress star(* 0.05 ** 0.01 *** 0.001) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM12a.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM12a.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. 
. * Analyses based on log-transformed VIINA-variable for wave 1 (results not printed in SOM.12)
. reg Comp_scale_1 ln_VIINA_W1_norm c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 i.sex c.age i.education, cluster(w1_q5)

{txt}Linear regression                               Number of obs     = {res}       998
                                                {txt}F(14, 23)         =  {res}    83.79
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1291
                                                {txt}Root MSE          =    {res} .13706

{txt}{ralign 102:(Std. err. adjusted for {res:24} clusters in {res:w1_q5})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Comp_scale_1{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ln_VIINA_W1_norm {c |}{col 38}{res}{space 2}-.0018457{col 50}{space 2} .0031777{col 61}{space 1}   -0.58{col 70}{space 3}0.567{col 78}{space 4}-.0084192{col 91}{space 3} .0047278
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2} -.045936{col 50}{space 2} .0285429{col 61}{space 1}   -1.61{col 70}{space 3}0.121{col 78}{space 4}-.1049815{col 91}{space 3} .0131095
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2} .0765568{col 50}{space 2} .0247587{col 61}{space 1}    3.09{col 70}{space 3}0.005{col 78}{space 4} .0253395{col 91}{space 3} .1277741
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2} .0595509{col 50}{space 2} .0265987{col 61}{space 1}    2.24{col 70}{space 3}0.035{col 78}{space 4} .0045273{col 91}{space 3} .1145745
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2} .0671073{col 50}{space 2} .0290721{col 61}{space 1}    2.31{col 70}{space 3}0.030{col 78}{space 4} .0069671{col 91}{space 3} .1272474
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2} .1351711{col 50}{space 2}  .048285{col 61}{space 1}    2.80{col 70}{space 3}0.010{col 78}{space 4}  .035286{col 91}{space 3} .2350562
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2} .0436148{col 50}{space 2} .0248253{col 61}{space 1}    1.76{col 70}{space 3}0.092{col 78}{space 4}-.0077402{col 91}{space 3} .0949698
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2}-.0058767{col 50}{space 2} .0328068{col 61}{space 1}   -0.18{col 70}{space 3}0.859{col 78}{space 4}-.0737427{col 91}{space 3} .0619894
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0233848{col 50}{space 2} .0081736{col 61}{space 1}    2.86{col 70}{space 3}0.009{col 78}{space 4} .0064764{col 91}{space 3} .0402932
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0001856{col 50}{space 2}  .000527{col 61}{space 1}   -0.35{col 70}{space 3}0.728{col 78}{space 4}-.0012757{col 91}{space 3} .0009045
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2} .0266658{col 50}{space 2} .0205398{col 61}{space 1}    1.30{col 70}{space 3}0.207{col 78}{space 4} -.015824{col 91}{space 3} .0691557
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2} .0414762{col 50}{space 2}  .020729{col 61}{space 1}    2.00{col 70}{space 3}0.057{col 78}{space 4}-.0014049{col 91}{space 3} .0843573
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2} .0411325{col 50}{space 2} .0232588{col 61}{space 1}    1.77{col 70}{space 3}0.090{col 78}{space 4} -.006982{col 91}{space 3} .0892469
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2} .0553801{col 50}{space 2} .0161017{col 61}{space 1}    3.44{col 70}{space 3}0.002{col 78}{space 4} .0220712{col 91}{space 3}  .088689
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5873941{col 50}{space 2} .0618648{col 61}{space 1}    9.49{col 70}{space 3}0.000{col 78}{space 4} .4594169{col 91}{space 3} .7153713
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_1 ln_VIINA_W1_norm c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 i.sex c.age i.education, cluster(w1_q5)

{txt}Linear regression                               Number of obs     = {res}       996
                                                {txt}F(14, 23)         =  {res}    27.01
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1120
                                                {txt}Root MSE          =    {res} .21547

{txt}{ralign 102:(Std. err. adjusted for {res:24} clusters in {res:w1_q5})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Warm_scale_1{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ln_VIINA_W1_norm {c |}{col 38}{res}{space 2}-.0003591{col 50}{space 2} .0035266{col 61}{space 1}   -0.10{col 70}{space 3}0.920{col 78}{space 4}-.0076544{col 91}{space 3} .0069363
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2} .0945964{col 50}{space 2} .0443955{col 61}{space 1}    2.13{col 70}{space 3}0.044{col 78}{space 4} .0027573{col 91}{space 3} .1864355
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2}-.0082757{col 50}{space 2} .0436312{col 61}{space 1}   -0.19{col 70}{space 3}0.851{col 78}{space 4}-.0985338{col 91}{space 3} .0819824
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2} .1043609{col 50}{space 2} .0645404{col 61}{space 1}    1.62{col 70}{space 3}0.120{col 78}{space 4} -.029151{col 91}{space 3} .2378728
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2}  .103574{col 50}{space 2} .0369416{col 61}{space 1}    2.80{col 70}{space 3}0.010{col 78}{space 4} .0271545{col 91}{space 3} .1799936
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2} .1014041{col 50}{space 2} .0722151{col 61}{space 1}    1.40{col 70}{space 3}0.174{col 78}{space 4}-.0479841{col 91}{space 3} .2507923
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2}  .065931{col 50}{space 2} .0318286{col 61}{space 1}    2.07{col 70}{space 3}0.050{col 78}{space 4} .0000886{col 91}{space 3} .1317734
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2}  .002571{col 50}{space 2} .0335688{col 61}{space 1}    0.08{col 70}{space 3}0.940{col 78}{space 4}-.0668714{col 91}{space 3} .0720134
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0200629{col 50}{space 2} .0148841{col 61}{space 1}    1.35{col 70}{space 3}0.191{col 78}{space 4}-.0107272{col 91}{space 3} .0508531
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0050409{col 50}{space 2} .0006165{col 61}{space 1}   -8.18{col 70}{space 3}0.000{col 78}{space 4}-.0063163{col 91}{space 3}-.0037655
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0061837{col 50}{space 2} .0263486{col 61}{space 1}   -0.23{col 70}{space 3}0.817{col 78}{space 4}-.0606899{col 91}{space 3} .0483225
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.0234977{col 50}{space 2} .0338063{col 61}{space 1}   -0.70{col 70}{space 3}0.494{col 78}{space 4}-.0934313{col 91}{space 3} .0464359
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.0605594{col 50}{space 2} .0311683{col 61}{space 1}   -1.94{col 70}{space 3}0.064{col 78}{space 4} -.125036{col 91}{space 3} .0039172
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.0532127{col 50}{space 2} .0256987{col 61}{space 1}   -2.07{col 70}{space 3}0.050{col 78}{space 4}-.1063745{col 91}{space 3}-.0000509
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}  .626588{col 50}{space 2} .0623239{col 61}{space 1}   10.05{col 70}{space 3}0.000{col 78}{space 4} .4976613{col 91}{space 3} .7555148
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_1 ln_VIINA_W1_norm c.fearfull_scale_1 c.aggressive_scale_1 c.sadness_scale_1 c.selfconf_scale_1 c.ID_Ukraine_1 c.ID_Europe_1 c.ID_Russia_1 i.sex c.age i.education, cluster(w1_q5)

{txt}Linear regression                               Number of obs     = {res}       995
                                                {txt}F(14, 23)         =  {res}    30.74
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0676
                                                {txt}Root MSE          =    {res} .25171

{txt}{ralign 102:(Std. err. adjusted for {res:24} clusters in {res:w1_q5})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Domi_scale_1{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ln_VIINA_W1_norm {c |}{col 38}{res}{space 2}-.0125897{col 50}{space 2} .0067036{col 61}{space 1}   -1.88{col 70}{space 3}0.073{col 78}{space 4}-.0264572{col 91}{space 3} .0012777
{txt}{space 20}fearfull_scale_1 {c |}{col 38}{res}{space 2}  .074113{col 50}{space 2} .0507521{col 61}{space 1}    1.46{col 70}{space 3}0.158{col 78}{space 4}-.0308758{col 91}{space 3} .1791017
{txt}{space 18}aggressive_scale_1 {c |}{col 38}{res}{space 2} .0667022{col 50}{space 2}  .037226{col 61}{space 1}    1.79{col 70}{space 3}0.086{col 78}{space 4}-.0103056{col 91}{space 3} .1437101
{txt}{space 21}sadness_scale_1 {c |}{col 38}{res}{space 2} .0342702{col 50}{space 2} .0646542{col 61}{space 1}    0.53{col 70}{space 3}0.601{col 78}{space 4}-.0994772{col 91}{space 3} .1680177
{txt}{space 20}selfconf_scale_1 {c |}{col 38}{res}{space 2}  .136295{col 50}{space 2} .0450642{col 61}{space 1}    3.02{col 70}{space 3}0.006{col 78}{space 4} .0430727{col 91}{space 3} .2295173
{txt}{space 24}ID_Ukraine_1 {c |}{col 38}{res}{space 2} .0118174{col 50}{space 2} .0439589{col 61}{space 1}    0.27{col 70}{space 3}0.790{col 78}{space 4}-.0791185{col 91}{space 3} .1027533
{txt}{space 25}ID_Europe_1 {c |}{col 38}{res}{space 2}-.0385554{col 50}{space 2} .0305963{col 61}{space 1}   -1.26{col 70}{space 3}0.220{col 78}{space 4}-.1018486{col 91}{space 3} .0247378
{txt}{space 25}ID_Russia_1 {c |}{col 38}{res}{space 2} .0760227{col 50}{space 2} .0321126{col 61}{space 1}    2.37{col 70}{space 3}0.027{col 78}{space 4} .0095927{col 91}{space 3} .1424526
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0971882{col 50}{space 2} .0155146{col 61}{space 1}   -6.26{col 70}{space 3}0.000{col 78}{space 4}-.1292826{col 91}{space 3}-.0650939
{txt}{space 33}age {c |}{col 38}{res}{space 2} .0021419{col 50}{space 2} .0011851{col 61}{space 1}    1.81{col 70}{space 3}0.084{col 78}{space 4}-.0003097{col 91}{space 3} .0045935
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}  .024921{col 50}{space 2} .0363144{col 61}{space 1}    0.69{col 70}{space 3}0.499{col 78}{space 4} -.050201{col 91}{space 3}  .100043
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.0279341{col 50}{space 2} .0338698{col 61}{space 1}   -0.82{col 70}{space 3}0.418{col 78}{space 4}-.0979992{col 91}{space 3} .0421309
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}  .036013{col 50}{space 2} .0342361{col 61}{space 1}    1.05{col 70}{space 3}0.304{col 78}{space 4}-.0348097{col 91}{space 3} .1068357
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2} .0431387{col 50}{space 2} .0259745{col 61}{space 1}    1.66{col 70}{space 3}0.110{col 78}{space 4}-.0105937{col 91}{space 3}  .096871
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .2647037{col 50}{space 2} .0822248{col 61}{space 1}    3.22{col 70}{space 3}0.004{col 78}{space 4} .0946087{col 91}{space 3} .4347987
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. **** Wave 2 - Produces Table SOM.12.b
. eststo clear
{txt}
{com}. 
. eststo: reg Comp_scale_2 c.VIINA_Oblast_attacks_W2 c.fearfull_scale_2 c.aggressive_scale_2 c.sadness_scale_2 c.selfconf_scale_2 c.ID_Ukraine_2 c.ID_Europe_2 c.ID_Russia_2 i.sex c.age i.education, cluster(w2_q4)

{txt}Linear regression                               Number of obs     = {res}       740
                                                {txt}F(14, 24)         =  {res}    42.09
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1241
                                                {txt}Root MSE          =    {res} .13133

{txt}{ralign 102:(Std. err. adjusted for {res:25} clusters in {res:w2_q4})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Comp_scale_2{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}VIINA_Oblast_attacks_W2 {c |}{col 38}{res}{space 2} .0013251{col 50}{space 2} .0101465{col 61}{space 1}    0.13{col 70}{space 3}0.897{col 78}{space 4}-.0196163{col 91}{space 3} .0222665
{txt}{space 20}fearfull_scale_2 {c |}{col 38}{res}{space 2}-.0855858{col 50}{space 2} .0172142{col 61}{space 1}   -4.97{col 70}{space 3}0.000{col 78}{space 4}-.1211142{col 91}{space 3}-.0500573
{txt}{space 18}aggressive_scale_2 {c |}{col 38}{res}{space 2} .0573742{col 50}{space 2} .0252004{col 61}{space 1}    2.28{col 70}{space 3}0.032{col 78}{space 4} .0053631{col 91}{space 3} .1093854
{txt}{space 21}sadness_scale_2 {c |}{col 38}{res}{space 2}  .086151{col 50}{space 2} .0257919{col 61}{space 1}    3.34{col 70}{space 3}0.003{col 78}{space 4} .0329192{col 91}{space 3} .1393828
{txt}{space 20}selfconf_scale_2 {c |}{col 38}{res}{space 2} .0622659{col 50}{space 2} .0289909{col 61}{space 1}    2.15{col 70}{space 3}0.042{col 78}{space 4} .0024317{col 91}{space 3} .1221001
{txt}{space 24}ID_Ukraine_2 {c |}{col 38}{res}{space 2} .1593679{col 50}{space 2} .0377353{col 61}{space 1}    4.22{col 70}{space 3}0.000{col 78}{space 4}  .081486{col 91}{space 3} .2372497
{txt}{space 25}ID_Europe_2 {c |}{col 38}{res}{space 2} .0496091{col 50}{space 2} .0259615{col 61}{space 1}    1.91{col 70}{space 3}0.068{col 78}{space 4}-.0039729{col 91}{space 3}  .103191
{txt}{space 25}ID_Russia_2 {c |}{col 38}{res}{space 2}-.0129365{col 50}{space 2} .0485734{col 61}{space 1}   -0.27{col 70}{space 3}0.792{col 78}{space 4} -.113187{col 91}{space 3} .0873141
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0257735{col 50}{space 2} .0077827{col 61}{space 1}    3.31{col 70}{space 3}0.003{col 78}{space 4} .0097108{col 91}{space 3} .0418362
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0005816{col 50}{space 2} .0005705{col 61}{space 1}   -1.02{col 70}{space 3}0.318{col 78}{space 4} -.001759{col 91}{space 3} .0005959
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0037926{col 50}{space 2} .0281257{col 61}{space 1}   -0.13{col 70}{space 3}0.894{col 78}{space 4}-.0618413{col 91}{space 3}  .054256
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2} .0080863{col 50}{space 2} .0273433{col 61}{space 1}    0.30{col 70}{space 3}0.770{col 78}{space 4}-.0483475{col 91}{space 3} .0645202
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2} .0166586{col 50}{space 2} .0255861{col 61}{space 1}    0.65{col 70}{space 3}0.521{col 78}{space 4}-.0361485{col 91}{space 3} .0694658
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2} .0193869{col 50}{space 2} .0229983{col 61}{space 1}    0.84{col 70}{space 3}0.408{col 78}{space 4}-.0280792{col 91}{space 3}  .066853
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6310441{col 50}{space 2} .0533802{col 61}{space 1}   11.82{col 70}{space 3}0.000{col 78}{space 4} .5208729{col 91}{space 3} .7412154
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. eststo: reg Warm_scale_2 c.VIINA_Oblast_attacks_W2 c.fearfull_scale_2 c.aggressive_scale_2 c.sadness_scale_2 c.selfconf_scale_2 c.ID_Ukraine_2 c.ID_Europe_2 c.ID_Russia_2 i.sex c.age i.education, cluster(w2_q4)

{txt}Linear regression                               Number of obs     = {res}       728
                                                {txt}F(14, 24)         =  {res}    24.64
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1206
                                                {txt}Root MSE          =    {res} .23544

{txt}{ralign 102:(Std. err. adjusted for {res:25} clusters in {res:w2_q4})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Warm_scale_2{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}VIINA_Oblast_attacks_W2 {c |}{col 38}{res}{space 2} .0191862{col 50}{space 2} .0202804{col 61}{space 1}    0.95{col 70}{space 3}0.354{col 78}{space 4}-.0226705{col 91}{space 3} .0610429
{txt}{space 20}fearfull_scale_2 {c |}{col 38}{res}{space 2} .1061628{col 50}{space 2} .0456334{col 61}{space 1}    2.33{col 70}{space 3}0.029{col 78}{space 4} .0119801{col 91}{space 3} .2003454
{txt}{space 18}aggressive_scale_2 {c |}{col 38}{res}{space 2}-.0685957{col 50}{space 2} .0511487{col 61}{space 1}   -1.34{col 70}{space 3}0.192{col 78}{space 4}-.1741615{col 91}{space 3}   .03697
{txt}{space 21}sadness_scale_2 {c |}{col 38}{res}{space 2} .0501229{col 50}{space 2} .0580696{col 61}{space 1}    0.86{col 70}{space 3}0.397{col 78}{space 4} -.069727{col 91}{space 3} .1699727
{txt}{space 20}selfconf_scale_2 {c |}{col 38}{res}{space 2} .1474133{col 50}{space 2} .0433574{col 61}{space 1}    3.40{col 70}{space 3}0.002{col 78}{space 4} .0579279{col 91}{space 3} .2368987
{txt}{space 24}ID_Ukraine_2 {c |}{col 38}{res}{space 2} .2016106{col 50}{space 2} .0588632{col 61}{space 1}    3.43{col 70}{space 3}0.002{col 78}{space 4}  .080123{col 91}{space 3} .3230982
{txt}{space 25}ID_Europe_2 {c |}{col 38}{res}{space 2} .0645799{col 50}{space 2} .0378985{col 61}{space 1}    1.70{col 70}{space 3}0.101{col 78}{space 4}-.0136388{col 91}{space 3} .1427986
{txt}{space 25}ID_Russia_2 {c |}{col 38}{res}{space 2} .0568291{col 50}{space 2} .0465705{col 61}{space 1}    1.22{col 70}{space 3}0.234{col 78}{space 4}-.0392877{col 91}{space 3} .1529459
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}   .03858{col 50}{space 2} .0155066{col 61}{space 1}    2.49{col 70}{space 3}0.020{col 78}{space 4} .0065759{col 91}{space 3} .0705841
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0064922{col 50}{space 2} .0009544{col 61}{space 1}   -6.80{col 70}{space 3}0.000{col 78}{space 4} -.008462{col 91}{space 3}-.0045223
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0502897{col 50}{space 2}  .037747{col 61}{space 1}   -1.33{col 70}{space 3}0.195{col 78}{space 4}-.1281957{col 91}{space 3} .0276164
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.0862869{col 50}{space 2} .0641405{col 61}{space 1}   -1.35{col 70}{space 3}0.191{col 78}{space 4}-.2186663{col 91}{space 3} .0460926
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.0627934{col 50}{space 2} .0461403{col 61}{space 1}   -1.36{col 70}{space 3}0.186{col 78}{space 4}-.1580222{col 91}{space 3} .0324355
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.0775398{col 50}{space 2}  .045346{col 61}{space 1}   -1.71{col 70}{space 3}0.100{col 78}{space 4}-.1711293{col 91}{space 3} .0160496
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5920556{col 50}{space 2} .0775404{col 61}{space 1}    7.64{col 70}{space 3}0.000{col 78}{space 4} .4320201{col 91}{space 3} .7520911
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. eststo: reg Domi_scale_2 c.VIINA_Oblast_attacks_W2 c.fearfull_scale_2 c.aggressive_scale_2 c.sadness_scale_2 c.selfconf_scale_2 c.ID_Ukraine_2 c.ID_Europe_2 c.ID_Russia_2 i.sex c.age i.education, cluster(w2_q4)

{txt}Linear regression                               Number of obs     = {res}       728
                                                {txt}F(14, 24)         =  {res}    13.90
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0562
                                                {txt}Root MSE          =    {res} .25348

{txt}{ralign 102:(Std. err. adjusted for {res:25} clusters in {res:w2_q4})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Domi_scale_2{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}VIINA_Oblast_attacks_W2 {c |}{col 38}{res}{space 2}-.0797794{col 50}{space 2} .0171241{col 61}{space 1}   -4.66{col 70}{space 3}0.000{col 78}{space 4}-.1151218{col 91}{space 3}-.0444369
{txt}{space 20}fearfull_scale_2 {c |}{col 38}{res}{space 2} .0712021{col 50}{space 2} .0453258{col 61}{space 1}    1.57{col 70}{space 3}0.129{col 78}{space 4}-.0223458{col 91}{space 3}   .16475
{txt}{space 18}aggressive_scale_2 {c |}{col 38}{res}{space 2} .0620388{col 50}{space 2} .0657703{col 61}{space 1}    0.94{col 70}{space 3}0.355{col 78}{space 4}-.0737044{col 91}{space 3}  .197782
{txt}{space 21}sadness_scale_2 {c |}{col 38}{res}{space 2} .0454363{col 50}{space 2} .0370624{col 61}{space 1}    1.23{col 70}{space 3}0.232{col 78}{space 4}-.0310567{col 91}{space 3} .1219293
{txt}{space 20}selfconf_scale_2 {c |}{col 38}{res}{space 2} .1046817{col 50}{space 2} .0510098{col 61}{space 1}    2.05{col 70}{space 3}0.051{col 78}{space 4}-.0005975{col 91}{space 3} .2099608
{txt}{space 24}ID_Ukraine_2 {c |}{col 38}{res}{space 2}-.0339921{col 50}{space 2} .0487065{col 61}{space 1}   -0.70{col 70}{space 3}0.492{col 78}{space 4}-.1345175{col 91}{space 3} .0665333
{txt}{space 25}ID_Europe_2 {c |}{col 38}{res}{space 2} .0057272{col 50}{space 2} .0433362{col 61}{space 1}    0.13{col 70}{space 3}0.896{col 78}{space 4}-.0837143{col 91}{space 3} .0951687
{txt}{space 25}ID_Russia_2 {c |}{col 38}{res}{space 2} .0355095{col 50}{space 2} .0786136{col 61}{space 1}    0.45{col 70}{space 3}0.656{col 78}{space 4} -.126741{col 91}{space 3}   .19776
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0913048{col 50}{space 2} .0183105{col 61}{space 1}   -4.99{col 70}{space 3}0.000{col 78}{space 4}-.1290958{col 91}{space 3}-.0535138
{txt}{space 33}age {c |}{col 38}{res}{space 2} .0006676{col 50}{space 2} .0010249{col 61}{space 1}    0.65{col 70}{space 3}0.521{col 78}{space 4}-.0014477{col 91}{space 3} .0027828
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0383892{col 50}{space 2} .0405658{col 61}{space 1}   -0.95{col 70}{space 3}0.353{col 78}{space 4}-.1221128{col 91}{space 3} .0453344
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.0746896{col 50}{space 2} .0531394{col 61}{space 1}   -1.41{col 70}{space 3}0.173{col 78}{space 4} -.184364{col 91}{space 3} .0349847
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.0241032{col 50}{space 2}  .035892{col 61}{space 1}   -0.67{col 70}{space 3}0.508{col 78}{space 4}-.0981808{col 91}{space 3} .0499743
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.0445354{col 50}{space 2} .0369568{col 61}{space 1}   -1.21{col 70}{space 3}0.240{col 78}{space 4}-.1208104{col 91}{space 3} .0317396
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4846569{col 50}{space 2} .0585157{col 61}{space 1}    8.28{col 70}{space 3}0.000{col 78}{space 4} .3638863{col 91}{space 3} .6054274
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. 
. esttab using TableSOM12b.rtf, se(3) b(3) ar2 onecell label nobaselevels title("Table SOM.12.b: OLS regression estimates of respondent ratings of importance of leader traits (competence, warmth, and dominance) as a function of oblast-level incidences of Russian attacks in two weeks leading up to Wave 2.") mtitle("Competence (model 1)" "Warmth (model 2)" "Dominance (model 3)") modelwidth() sfmt(0) replace compress star(* 0.05 ** 0.01 *** 0.001) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM12b.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM12b.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. 
. * Analyses based on log-transformed VIINA-variable for wave 2 (results not printed in SOM.12)
. reg Comp_scale_2 ln_VIINA_W2_norm c.fearfull_scale_2 c.aggressive_scale_2 c.sadness_scale_2 c.selfconf_scale_2 c.ID_Ukraine_2 c.ID_Europe_2 c.ID_Russia_2 i.sex c.age i.education, cluster(w2_q4)

{txt}Linear regression                               Number of obs     = {res}       727
                                                {txt}F(14, 23)         =  {res}    32.15
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1339
                                                {txt}Root MSE          =    {res} .13041

{txt}{ralign 102:(Std. err. adjusted for {res:24} clusters in {res:w2_q4})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Comp_scale_2{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ln_VIINA_W2_norm {c |}{col 38}{res}{space 2} .0025742{col 50}{space 2} .0028938{col 61}{space 1}    0.89{col 70}{space 3}0.383{col 78}{space 4}-.0034122{col 91}{space 3} .0085605
{txt}{space 20}fearfull_scale_2 {c |}{col 38}{res}{space 2}   -.0855{col 50}{space 2} .0180433{col 61}{space 1}   -4.74{col 70}{space 3}0.000{col 78}{space 4}-.1228254{col 91}{space 3}-.0481745
{txt}{space 18}aggressive_scale_2 {c |}{col 38}{res}{space 2} .0579847{col 50}{space 2} .0260042{col 61}{space 1}    2.23{col 70}{space 3}0.036{col 78}{space 4} .0041909{col 91}{space 3} .1117786
{txt}{space 21}sadness_scale_2 {c |}{col 38}{res}{space 2} .0892843{col 50}{space 2} .0265635{col 61}{space 1}    3.36{col 70}{space 3}0.003{col 78}{space 4} .0343335{col 91}{space 3} .1442351
{txt}{space 20}selfconf_scale_2 {c |}{col 38}{res}{space 2} .0692507{col 50}{space 2} .0285262{col 61}{space 1}    2.43{col 70}{space 3}0.023{col 78}{space 4} .0102396{col 91}{space 3} .1282617
{txt}{space 24}ID_Ukraine_2 {c |}{col 38}{res}{space 2} .1648015{col 50}{space 2} .0379748{col 61}{space 1}    4.34{col 70}{space 3}0.000{col 78}{space 4} .0862445{col 91}{space 3} .2433584
{txt}{space 25}ID_Europe_2 {c |}{col 38}{res}{space 2} .0481153{col 50}{space 2} .0264057{col 61}{space 1}    1.82{col 70}{space 3}0.081{col 78}{space 4} -.006509{col 91}{space 3} .1027396
{txt}{space 25}ID_Russia_2 {c |}{col 38}{res}{space 2}-.0153309{col 50}{space 2} .0485989{col 61}{space 1}   -0.32{col 70}{space 3}0.755{col 78}{space 4}-.1158653{col 91}{space 3} .0852034
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0276918{col 50}{space 2}  .007823{col 61}{space 1}    3.54{col 70}{space 3}0.002{col 78}{space 4} .0115087{col 91}{space 3} .0438749
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0006752{col 50}{space 2} .0005732{col 61}{space 1}   -1.18{col 70}{space 3}0.251{col 78}{space 4} -.001861{col 91}{space 3} .0005106
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0040943{col 50}{space 2} .0285078{col 61}{space 1}   -0.14{col 70}{space 3}0.887{col 78}{space 4}-.0630672{col 91}{space 3} .0548786
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2} .0084034{col 50}{space 2} .0272693{col 61}{space 1}    0.31{col 70}{space 3}0.761{col 78}{space 4}-.0480074{col 91}{space 3} .0648143
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2} .0169186{col 50}{space 2} .0257265{col 61}{space 1}    0.66{col 70}{space 3}0.517{col 78}{space 4}-.0363007{col 91}{space 3}  .070138
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2} .0195904{col 50}{space 2} .0232152{col 61}{space 1}    0.84{col 70}{space 3}0.407{col 78}{space 4} -.028434{col 91}{space 3} .0676148
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .6283565{col 50}{space 2} .0525998{col 61}{space 1}   11.95{col 70}{space 3}0.000{col 78}{space 4} .5195454{col 91}{space 3} .7371675
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_2 ln_VIINA_W2_norm c.fearfull_scale_2 c.aggressive_scale_2 c.sadness_scale_2 c.selfconf_scale_2 c.ID_Ukraine_2 c.ID_Europe_2 c.ID_Russia_2 i.sex c.age i.education, cluster(w2_q4)

{txt}Linear regression                               Number of obs     = {res}       715
                                                {txt}F(14, 23)         =  {res}    26.02
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1240
                                                {txt}Root MSE          =    {res} .23538

{txt}{ralign 102:(Std. err. adjusted for {res:24} clusters in {res:w2_q4})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Warm_scale_2{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ln_VIINA_W2_norm {c |}{col 38}{res}{space 2} .0029692{col 50}{space 2} .0060121{col 61}{space 1}    0.49{col 70}{space 3}0.626{col 78}{space 4}-.0094679{col 91}{space 3} .0154062
{txt}{space 20}fearfull_scale_2 {c |}{col 38}{res}{space 2} .1032017{col 50}{space 2}  .044984{col 61}{space 1}    2.29{col 70}{space 3}0.031{col 78}{space 4} .0101451{col 91}{space 3} .1962582
{txt}{space 18}aggressive_scale_2 {c |}{col 38}{res}{space 2}-.0807987{col 50}{space 2} .0504046{col 61}{space 1}   -1.60{col 70}{space 3}0.123{col 78}{space 4}-.1850684{col 91}{space 3} .0234711
{txt}{space 21}sadness_scale_2 {c |}{col 38}{res}{space 2} .0592763{col 50}{space 2} .0566496{col 61}{space 1}    1.05{col 70}{space 3}0.306{col 78}{space 4}-.0579123{col 91}{space 3} .1764649
{txt}{space 20}selfconf_scale_2 {c |}{col 38}{res}{space 2} .1550581{col 50}{space 2} .0433985{col 61}{space 1}    3.57{col 70}{space 3}0.002{col 78}{space 4} .0652815{col 91}{space 3} .2448347
{txt}{space 24}ID_Ukraine_2 {c |}{col 38}{res}{space 2} .2068077{col 50}{space 2} .0590759{col 61}{space 1}    3.50{col 70}{space 3}0.002{col 78}{space 4} .0845998{col 91}{space 3} .3290156
{txt}{space 25}ID_Europe_2 {c |}{col 38}{res}{space 2} .0634301{col 50}{space 2} .0383242{col 61}{space 1}    1.66{col 70}{space 3}0.111{col 78}{space 4}-.0158496{col 91}{space 3} .1427097
{txt}{space 25}ID_Russia_2 {c |}{col 38}{res}{space 2} .0493542{col 50}{space 2} .0452149{col 61}{space 1}    1.09{col 70}{space 3}0.286{col 78}{space 4}-.0441801{col 91}{space 3} .1428884
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2} .0408103{col 50}{space 2} .0156274{col 61}{space 1}    2.61{col 70}{space 3}0.016{col 78}{space 4} .0084827{col 91}{space 3}  .073138
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0064855{col 50}{space 2} .0009774{col 61}{space 1}   -6.64{col 70}{space 3}0.000{col 78}{space 4}-.0085073{col 91}{space 3}-.0044637
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0507233{col 50}{space 2} .0378916{col 61}{space 1}   -1.34{col 70}{space 3}0.194{col 78}{space 4} -.129108{col 91}{space 3} .0276615
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.0861958{col 50}{space 2} .0636228{col 61}{space 1}   -1.35{col 70}{space 3}0.189{col 78}{space 4}-.2178096{col 91}{space 3}  .045418
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.0589737{col 50}{space 2} .0466465{col 61}{space 1}   -1.26{col 70}{space 3}0.219{col 78}{space 4}-.1554695{col 91}{space 3}  .037522
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.0791958{col 50}{space 2} .0451556{col 61}{space 1}   -1.75{col 70}{space 3}0.093{col 78}{space 4}-.1726073{col 91}{space 3} .0142158
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .5983831{col 50}{space 2} .0800611{col 61}{space 1}    7.47{col 70}{space 3}0.000{col 78}{space 4} .4327642{col 91}{space 3} .7640021
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_2 ln_VIINA_W2_norm c.fearfull_scale_2 c.aggressive_scale_2 c.sadness_scale_2 c.selfconf_scale_2 c.ID_Ukraine_2 c.ID_Europe_2 c.ID_Russia_2 i.sex c.age i.education, cluster(w2_q4)

{txt}Linear regression                               Number of obs     = {res}       715
                                                {txt}F(14, 23)         =  {res}    18.78
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0568
                                                {txt}Root MSE          =    {res} .25407

{txt}{ralign 102:(Std. err. adjusted for {res:24} clusters in {res:w2_q4})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        Domi_scale_2{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      t{col 70}   P>|t|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ln_VIINA_W2_norm {c |}{col 38}{res}{space 2}-.0155218{col 50}{space 2} .0046615{col 61}{space 1}   -3.33{col 70}{space 3}0.003{col 78}{space 4}-.0251647{col 91}{space 3}-.0058788
{txt}{space 20}fearfull_scale_2 {c |}{col 38}{res}{space 2} .0834996{col 50}{space 2} .0441809{col 61}{space 1}    1.89{col 70}{space 3}0.071{col 78}{space 4}-.0078956{col 91}{space 3} .1748948
{txt}{space 18}aggressive_scale_2 {c |}{col 38}{res}{space 2} .0548506{col 50}{space 2} .0663899{col 61}{space 1}    0.83{col 70}{space 3}0.417{col 78}{space 4}-.0824873{col 91}{space 3} .1921885
{txt}{space 21}sadness_scale_2 {c |}{col 38}{res}{space 2}   .02757{col 50}{space 2} .0374698{col 61}{space 1}    0.74{col 70}{space 3}0.469{col 78}{space 4}-.0499422{col 91}{space 3} .1050822
{txt}{space 20}selfconf_scale_2 {c |}{col 38}{res}{space 2} .1010124{col 50}{space 2} .0517046{col 61}{space 1}    1.95{col 70}{space 3}0.063{col 78}{space 4}-.0059467{col 91}{space 3} .2079715
{txt}{space 24}ID_Ukraine_2 {c |}{col 38}{res}{space 2}-.0267146{col 50}{space 2} .0502332{col 61}{space 1}   -0.53{col 70}{space 3}0.600{col 78}{space 4}  -.13063{col 91}{space 3} .0772008
{txt}{space 25}ID_Europe_2 {c |}{col 38}{res}{space 2} .0081026{col 50}{space 2} .0434242{col 61}{space 1}    0.19{col 70}{space 3}0.854{col 78}{space 4}-.0817273{col 91}{space 3} .0979325
{txt}{space 25}ID_Russia_2 {c |}{col 38}{res}{space 2} .0341445{col 50}{space 2} .0798484{col 61}{space 1}    0.43{col 70}{space 3}0.673{col 78}{space 4}-.1310345{col 91}{space 3} .1993235
{txt}{space 36} {c |}
{space 33}sex {c |}
{space 29}Female  {c |}{col 38}{res}{space 2}-.0956663{col 50}{space 2} .0182986{col 61}{space 1}   -5.23{col 70}{space 3}0.000{col 78}{space 4}-.1335197{col 91}{space 3}-.0578128
{txt}{space 33}age {c |}{col 38}{res}{space 2} .0005498{col 50}{space 2} .0010328{col 61}{space 1}    0.53{col 70}{space 3}0.600{col 78}{space 4}-.0015866{col 91}{space 3} .0026863
{txt}{space 36} {c |}
{space 27}education {c |}
Professional-technical (vocational)  {c |}{col 38}{res}{space 2}-.0399239{col 50}{space 2} .0412829{col 61}{space 1}   -0.97{col 70}{space 3}0.344{col 78}{space 4}-.1253241{col 91}{space 3} .0454762
{txt}{space 18}Incomplete higher  {c |}{col 38}{res}{space 2}-.0734172{col 50}{space 2} .0539684{col 61}{space 1}   -1.36{col 70}{space 3}0.187{col 78}{space 4}-.1850594{col 91}{space 3}  .038225
{txt}{space 20}Bachelor degree  {c |}{col 38}{res}{space 2}-.0244657{col 50}{space 2} .0360509{col 61}{space 1}   -0.68{col 70}{space 3}0.504{col 78}{space 4}-.0990428{col 91}{space 3} .0501113
{txt}{space 10}Master degree & Doctorate  {c |}{col 38}{res}{space 2}-.0412472{col 50}{space 2} .0374474{col 61}{space 1}   -1.10{col 70}{space 3}0.282{col 78}{space 4} -.118713{col 91}{space 3} .0362186
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} .4383067{col 50}{space 2} .0555308{col 61}{space 1}    7.89{col 70}{space 3}0.000{col 78}{space 4} .3234325{col 91}{space 3} .5531809
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. 
. **************************************************************************************************************************************************
. ********************************************************** RESHAPES DATASET TO LONG FORMAT *******************************************************
. **************************************************************************************************************************************************
. reshape long Competence_ Trustworthy_ Dominant_ Generous_ Strong_ Warm_ Toughminded_ Comp_scale_ Warm_scale_ Domi_scale_ Comp_PCA_ Warm_PCA_ Domi_PCA_ ///
> afraid_ frightened_ scared_ angry_ hostile_ disgusted_ sad_ lonely_ downhearted_ proud_ strong_ confident_ anxiety_scale_ aggressive_scale_ sadness_scale_ selfconf_scale_ Victimization_ ///
> Conflict_ ID_Ukraine_ ID_Russia_ ID_Europe_, i(ID_random) j(wave)
{txt}(j = 1 2)
(variable {bf:anxiety_scale_1} not found)
(variable {bf:anxiety_scale_2} not found)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}       1,081   {txt}->   {res}2,162       
{txt}Number of variables        {res}         316   {txt}->   {res}285         
{txt}j variable (2 values)                     ->   {res}wave
{txt}xij variables:
              {res}Competence_1 Competence_2   {txt}->   {res}Competence_
            Trustworthy_1 Trustworthy_2   {txt}->   {res}Trustworthy_
                  Dominant_1 Dominant_2   {txt}->   {res}Dominant_
                  Generous_1 Generous_2   {txt}->   {res}Generous_
                      Strong_1 Strong_2   {txt}->   {res}Strong_
                          Warm_1 Warm_2   {txt}->   {res}Warm_
            Toughminded_1 Toughminded_2   {txt}->   {res}Toughminded_
              Comp_scale_1 Comp_scale_2   {txt}->   {res}Comp_scale_
              Warm_scale_1 Warm_scale_2   {txt}->   {res}Warm_scale_
              Domi_scale_1 Domi_scale_2   {txt}->   {res}Domi_scale_
                  Comp_PCA_1 Comp_PCA_2   {txt}->   {res}Comp_PCA_
                  Warm_PCA_1 Warm_PCA_2   {txt}->   {res}Warm_PCA_
                  Domi_PCA_1 Domi_PCA_2   {txt}->   {res}Domi_PCA_
                      afraid_1 afraid_2   {txt}->   {res}afraid_
              frightened_1 frightened_2   {txt}->   {res}frightened_
                      scared_1 scared_2   {txt}->   {res}scared_
                        angry_1 angry_2   {txt}->   {res}angry_
                    hostile_1 hostile_2   {txt}->   {res}hostile_
                disgusted_1 disgusted_2   {txt}->   {res}disgusted_
                            sad_1 sad_2   {txt}->   {res}sad_
                      lonely_1 lonely_2   {txt}->   {res}lonely_
            downhearted_1 downhearted_2   {txt}->   {res}downhearted_
                        proud_1 proud_2   {txt}->   {res}proud_
                      strong_1 strong_2   {txt}->   {res}strong_
                confident_1 confident_2   {txt}->   {res}confident_
        anxiety_scale_1 anxiety_scale_2   {txt}->   {res}anxiety_scale_
  aggressive_scale_1 aggressive_scale_2   {txt}->   {res}aggressive_scale_
        sadness_scale_1 sadness_scale_2   {txt}->   {res}sadness_scale_
      selfconf_scale_1 selfconf_scale_2   {txt}->   {res}selfconf_scale_
        Victimization_1 Victimization_2   {txt}->   {res}Victimization_
                  Conflict_1 Conflict_2   {txt}->   {res}Conflict_
              ID_Ukraine_1 ID_Ukraine_2   {txt}->   {res}ID_Ukraine_
                ID_Russia_1 ID_Russia_2   {txt}->   {res}ID_Russia_
                ID_Europe_1 ID_Europe_2   {txt}->   {res}ID_Europe_
{txt}{hline 77}

{com}. 
. ** Labels survey round variable
. label define waveLB 1 "Wave 1" 2 "Wave 2"
{txt}
{com}. label values wave waveLB
{txt}
{com}. 
. *** Sets panelvar to ID_random
. xtset ID_random

{txt}{col 1}Panel variable: {res}ID_random{txt} (balanced)

{com}. 
. 
. ********************************************** MAPPING WARTIME LEADER TRAIT PREFERENCES **********************************************************
. *** Produces Figure 1
. reg Comp_scale_ i.wave if Context== 1 & include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       748
                                                {txt}F(1, 373)         =  {res}     1.84
                                                {txt}Prob > F          = {res}    0.1756
                                                {txt}R-squared         = {res}    0.0014
                                                {txt}Root MSE          =    {res} .13531

{txt}{ralign 78:(Std. err. adjusted for {res:374} clusters in {res:ID_random})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} Comp_scale_{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}wave {c |}
{space 5}Wave 2  {c |}{col 14}{res}{space 2} .0101753{col 26}{space 2} .0074981{col 37}{space 1}    1.36{col 46}{space 3}0.176{col 54}{space 4}-.0045685{col 67}{space 3}  .024919
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8989899{col 26}{space 2} .0071547{col 37}{space 1}  125.65{col 46}{space 3}0.000{col 54}{space 4} .8849212{col 67}{space 3} .9130586
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. test _cons == .7034314

{p 0 7}{space 1}{text:( 1)}{space 1} {res}_cons = .7034314{p_end}

{txt}       F(  1,   373) ={res}  747.08
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. test _cons == .5274064

{p 0 7}{space 1}{text:( 1)}{space 1} {res}_cons = .5274064{p_end}

{txt}       F(  1,   373) ={res} 2697.27
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. margins, at(wave=(1 2)) level(95)
{res}
{txt}{col 1}Adjusted predictions{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:748}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 4:wave} = {res:{ralign 1:1}}
{lalign 7:2._at: }{space 0}{lalign 4:wave} = {res:{ralign 1:2}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .8989899{col 26}{space 2} .0071547{col 37}{space 1}  125.65{col 46}{space 3}0.000{col 54}{space 4} .8849212{col 67}{space 3} .9130586
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .9091652{col 26}{space 2} .0068442{col 37}{space 1}  132.84{col 46}{space 3}0.000{col 54}{space 4} .8957071{col 67}{space 3} .9226233
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(0(.1)1)) ylabel(0(.1)1) recast(scatter) yline(0) plotopts(mcolor(black) msize(small)) ciopts(lcolor(black) lwidth(thin)) ///
> xtitle() ytitle("Competence Importance") title("Competence") legend(off) scheme(s1mono) name(Comp_war_fig1, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:wave}{p_end}
{res}{txt}
{com}. 
. reg Warm_scale_ i.wave if Context== 1 & include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       748
                                                {txt}F(1, 373)         =  {res}     8.36
                                                {txt}Prob > F          = {res}    0.0041
                                                {txt}R-squared         = {res}    0.0049
                                                {txt}Root MSE          =    {res} .24422

{txt}{ralign 78:(Std. err. adjusted for {res:374} clusters in {res:ID_random})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} Warm_scale_{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}wave {c |}
{space 5}Wave 2  {c |}{col 14}{res}{space 2}-.0343137{col 26}{space 2}   .01187{col 37}{space 1}   -2.89{col 46}{space 3}0.004{col 54}{space 4}-.0576542{col 67}{space 3}-.0109732
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7034314{col 26}{space 2} .0118986{col 37}{space 1}   59.12{col 46}{space 3}0.000{col 54}{space 4} .6800347{col 67}{space 3}  .726828
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. test _cons == .8989899

{p 0 7}{space 1}{text:( 1)}{space 1} {res}_cons = .8989899{p_end}

{txt}       F(  1,   373) ={res}  270.13
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. test _cons == .5274064

{p 0 7}{space 1}{text:( 1)}{space 1} {res}_cons = .5274064{p_end}

{txt}       F(  1,   373) ={res}  218.86
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. margins, at(wave=(1 2)) level(95)
{res}
{txt}{col 1}Adjusted predictions{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:748}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 4:wave} = {res:{ralign 1:1}}
{lalign 7:2._at: }{space 0}{lalign 4:wave} = {res:{ralign 1:2}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .7034314{col 26}{space 2} .0118986{col 37}{space 1}   59.12{col 46}{space 3}0.000{col 54}{space 4} .6800347{col 67}{space 3}  .726828
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6691176{col 26}{space 2} .0133337{col 37}{space 1}   50.18{col 46}{space 3}0.000{col 54}{space 4}  .642899{col 67}{space 3} .6953363
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(0(.1)1)) ylabel(0(.1)1) recast(scatter) yline(0) plotopts(mcolor(cranberry) msize(small)) ciopts(lcolor(cranberry) lwidth(thin)) ///
> xtitle() ytitle("Warmth Importance") title("Warmth") legend(off)  scheme(s1mono) name(Warmth_war_fig1, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:wave}{p_end}
{res}{txt}
{com}. 
. reg Domi_scale_ i.wave if Context== 1 & include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       748
                                                {txt}F(1, 373)         =  {res}     0.29
                                                {txt}Prob > F          = {res}    0.5897
                                                {txt}R-squared         = {res}    0.0001
                                                {txt}Root MSE          =    {res} .26573

{txt}{ralign 78:(Std. err. adjusted for {res:374} clusters in {res:ID_random})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} Domi_scale_{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}wave {c |}
{space 5}Wave 2  {c |}{col 14}{res}{space 2}-.0062389{col 26}{space 2} .0115579{col 37}{space 1}   -0.54{col 46}{space 3}0.590{col 54}{space 4}-.0289657{col 67}{space 3}  .016488
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5274064{col 26}{space 2} .0134822{col 37}{space 1}   39.12{col 46}{space 3}0.000{col 54}{space 4} .5008958{col 67}{space 3}  .553917
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. test _cons == .8989899

{p 0 7}{space 1}{text:( 1)}{space 1} {res}_cons = .8989899{p_end}

{txt}       F(  1,   373) ={res}  759.61
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. test _cons == .7034314

{p 0 7}{space 1}{text:( 1)}{space 1} {res}_cons = .7034314{p_end}

{txt}       F(  1,   373) ={res}  170.46
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. margins, at(wave=(1 2)) level(95)
{res}
{txt}{col 1}Adjusted predictions{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:748}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 4:wave} = {res:{ralign 1:1}}
{lalign 7:2._at: }{space 0}{lalign 4:wave} = {res:{ralign 1:2}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5274064{col 26}{space 2} .0134822{col 37}{space 1}   39.12{col 46}{space 3}0.000{col 54}{space 4} .5008958{col 67}{space 3}  .553917
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5211676{col 26}{space 2} .0140122{col 37}{space 1}   37.19{col 46}{space 3}0.000{col 54}{space 4} .4936148{col 67}{space 3} .5487203
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot,  recastci(rcap) yscale(range(0(.1)1)) ylabel(0(.1)1) recast(scatter) yline(0) plotopts(mcolor(navy) msize(small)) ciopts(lcolor(navy) lwidth(thin)) ///
> xtitle() ytitle("Dominance Importance") title("Dominance") legend(off)  scheme(s1mono) name(Domi_war_fig1, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:wave}{p_end}
{res}{txt}
{com}. 
. graph combine Comp_war_fig1 Warmth_war_fig1 Domi_war_fig1, scheme(s1mono) cols(3)
{res}{txt}
{com}. graph export Figure1.pdf, replace
{txt}{p 0 4 2}
file {bf}
Figure1.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. *** Produces Table SOM.3
. eststo clear
{txt}
{com}. 
. eststo: reg Comp_scale_ i.wave if Context== 1 & include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       748
                                                {txt}F(1, 373)         =  {res}     1.84
                                                {txt}Prob > F          = {res}    0.1756
                                                {txt}R-squared         = {res}    0.0014
                                                {txt}Root MSE          =    {res} .13531

{txt}{ralign 78:(Std. err. adjusted for {res:374} clusters in {res:ID_random})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} Comp_scale_{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}wave {c |}
{space 5}Wave 2  {c |}{col 14}{res}{space 2} .0101753{col 26}{space 2} .0074981{col 37}{space 1}    1.36{col 46}{space 3}0.176{col 54}{space 4}-.0045685{col 67}{space 3}  .024919
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8989899{col 26}{space 2} .0071547{col 37}{space 1}  125.65{col 46}{space 3}0.000{col 54}{space 4} .8849212{col 67}{space 3} .9130586
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. eststo: reg Warm_scale_ i.wave if Context== 1 & include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       748
                                                {txt}F(1, 373)         =  {res}     8.36
                                                {txt}Prob > F          = {res}    0.0041
                                                {txt}R-squared         = {res}    0.0049
                                                {txt}Root MSE          =    {res} .24422

{txt}{ralign 78:(Std. err. adjusted for {res:374} clusters in {res:ID_random})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} Warm_scale_{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}wave {c |}
{space 5}Wave 2  {c |}{col 14}{res}{space 2}-.0343137{col 26}{space 2}   .01187{col 37}{space 1}   -2.89{col 46}{space 3}0.004{col 54}{space 4}-.0576542{col 67}{space 3}-.0109732
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7034314{col 26}{space 2} .0118986{col 37}{space 1}   59.12{col 46}{space 3}0.000{col 54}{space 4} .6800347{col 67}{space 3}  .726828
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. eststo: reg Domi_scale_ i.wave if Context== 1 & include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       748
                                                {txt}F(1, 373)         =  {res}     0.29
                                                {txt}Prob > F          = {res}    0.5897
                                                {txt}R-squared         = {res}    0.0001
                                                {txt}Root MSE          =    {res} .26573

{txt}{ralign 78:(Std. err. adjusted for {res:374} clusters in {res:ID_random})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} Domi_scale_{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}wave {c |}
{space 5}Wave 2  {c |}{col 14}{res}{space 2}-.0062389{col 26}{space 2} .0115579{col 37}{space 1}   -0.54{col 46}{space 3}0.590{col 54}{space 4}-.0289657{col 67}{space 3}  .016488
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5274064{col 26}{space 2} .0134822{col 37}{space 1}   39.12{col 46}{space 3}0.000{col 54}{space 4} .5008958{col 67}{space 3}  .553917
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. 
. esttab using TableSOM3.rtf, se(3) b(3) ar2 onecell label nobaselevels title("Table SOM.3: OLS regression estimates of the change in rated importance of leader competence, warmth, and dominance over two survey waves.") mtitle("Competence (model 1)" "Warmth (model 2)" "Dominance (model 3)") modelwidth() sfmt(0) replace compress star(* 0.05 ** 0.01 *** 0.001) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM3.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM3.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. 
. ************************************************** TESTING THE CONFLICT-SENSITIVITY HYPOTHESIS ***************************************************
. *** Within-respondent test of the conflict-sensitivity hypothesis: Testing if trait preferences change across waves for respondents assigned to the peace condition in wave 1
. * Produces results reported in main text and with full models in SOM.4b
. eststo clear
{txt}
{com}. 
. eststo: reg Comp_scale_ i.wave if include==1 & Context == 2, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       758
                                                {txt}F(1, 378)         =  {res}     2.07
                                                {txt}Prob > F          = {res}    0.1506
                                                {txt}R-squared         = {res}    0.0015
                                                {txt}Root MSE          =    {res} .12291

{txt}{ralign 78:(Std. err. adjusted for {res:379} clusters in {res:ID_random})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} Comp_scale_{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}wave {c |}
{space 5}Wave 2  {c |}{col 14}{res}{space 2}-.0093814{col 26}{space 2} .0065135{col 37}{space 1}   -1.44{col 46}{space 3}0.151{col 54}{space 4}-.0221887{col 67}{space 3} .0034259
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .9166667{col 26}{space 2} .0058095{col 37}{space 1}  157.79{col 46}{space 3}0.000{col 54}{space 4} .9052437{col 67}{space 3} .9280896
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. eststo: reg Warm_scale_ i.wave if include==1 & Context == 2, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       758
                                                {txt}F(1, 378)         =  {res}    28.81
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0167
                                                {txt}Root MSE          =    {res} .22356

{txt}{ralign 78:(Std. err. adjusted for {res:379} clusters in {res:ID_random})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} Warm_scale_{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}wave {c |}
{space 5}Wave 2  {c |}{col 14}{res}{space 2}-.0582674{col 26}{space 2} .0108552{col 37}{space 1}   -5.37{col 46}{space 3}0.000{col 54}{space 4}-.0796116{col 67}{space 3}-.0369232
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7365875{col 26}{space 2} .0109741{col 37}{space 1}   67.12{col 46}{space 3}0.000{col 54}{space 4} .7150095{col 67}{space 3} .7581655
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. eststo: reg Domi_scale_ i.wave if include==1 & Context == 2, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       758
                                                {txt}F(1, 378)         =  {res}    16.19
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0093
                                                {txt}Root MSE          =    {res} .24655

{txt}{ralign 78:(Std. err. adjusted for {res:379} clusters in {res:ID_random})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} Domi_scale_{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}wave {c |}
{space 5}Wave 2  {c |}{col 14}{res}{space 2} .0477133{col 26}{space 2} .0118576{col 37}{space 1}    4.02{col 46}{space 3}0.000{col 54}{space 4} .0243982{col 67}{space 3} .0710284
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4872471{col 26}{space 2} .0127499{col 37}{space 1}   38.22{col 46}{space 3}0.000{col 54}{space 4} .4621776{col 67}{space 3} .5123167
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. 
. esttab using TableSOM4b.rtf, se(3) b(3) ar2 onecell label nobaselevels title("Table SOM.4.b: OLS regression estimates of importance of leader trait dimensions (competence, warmth, and dominance) as a function of experimental condition (peace vs. war) when the sample is restricted to participants assigned to peace condition in Wave 1.") mtitle("Competence (model 1)" "Warmth (model 2)" "Dominance (model 3)") modelwidth() sfmt(0) replace compress star(* 0.05 ** 0.01 *** 0.001) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM4b.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM4b.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. * Testing full interactions between assigned experimental condition (assigned in wave 1) and wave (all respondents assigned to think of the ongoing war in wave 2)
. reg Comp_scale_ i.wave##ib(2).Context if include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}     1,506
                                                {txt}F(3, 752)         =  {res}     1.72
                                                {txt}Prob > F          = {res}    0.1613
                                                {txt}R-squared         = {res}    0.0024
                                                {txt}Root MSE          =    {res} .12921

{txt}{ralign 87:(Std. err. adjusted for {res:753} clusters in {res:ID_random})}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}          Comp_scale_{col 23}{c |} Coefficient{col 35}  std. err.{col 47}      t{col 55}   P>|t|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}wave {c |}
{space 14}Wave 2  {c |}{col 23}{res}{space 2}-.0093814{col 35}{space 2} .0065115{col 46}{space 1}   -1.44{col 55}{space 3}0.150{col 63}{space 4}-.0221642{col 76}{space 3} .0034014
{txt}{space 21} {c |}
{space 14}Context {c |}
{space 7}Conflict, now  {c |}{col 23}{res}{space 2}-.0176768{col 35}{space 2} .0092132{col 46}{space 1}   -1.92{col 55}{space 3}0.055{col 63}{space 4}-.0357634{col 76}{space 3} .0004099
{txt}{space 21} {c |}
{space 9}wave#Context {c |}
Wave 2#Conflict, now  {c |}{col 23}{res}{space 2} .0195567{col 35}{space 2} .0099288{col 46}{space 1}    1.97{col 55}{space 3}0.049{col 63}{space 4} .0000652{col 76}{space 3} .0390481
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} .9166667{col 35}{space 2} .0058076{col 46}{space 1}  157.84{col 55}{space 3}0.000{col 63}{space 4} .9052656{col 76}{space 3} .9280677
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_scale_ i.wave##ib(2).Context if include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}     1,506
                                                {txt}F(3, 752)         =  {res}    13.15
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0124
                                                {txt}Root MSE          =    {res} .23405

{txt}{ralign 87:(Std. err. adjusted for {res:753} clusters in {res:ID_random})}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}          Warm_scale_{col 23}{c |} Coefficient{col 35}  std. err.{col 47}      t{col 55}   P>|t|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}wave {c |}
{space 14}Wave 2  {c |}{col 23}{res}{space 2}-.0582674{col 35}{space 2} .0108517{col 46}{space 1}   -5.37{col 55}{space 3}0.000{col 63}{space 4}-.0795707{col 76}{space 3} -.036964
{txt}{space 21} {c |}
{space 14}Context {c |}
{space 7}Conflict, now  {c |}{col 23}{res}{space 2}-.0331561{col 35}{space 2} .0161812{col 46}{space 1}   -2.05{col 55}{space 3}0.041{col 63}{space 4}-.0649219{col 76}{space 3}-.0013904
{txt}{space 21} {c |}
{space 9}wave#Context {c |}
Wave 2#Conflict, now  {c |}{col 23}{res}{space 2} .0239536{col 35}{space 2} .0160798{col 46}{space 1}    1.49{col 55}{space 3}0.137{col 63}{space 4} -.007613{col 76}{space 3} .0555203
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} .7365875{col 35}{space 2} .0109706{col 46}{space 1}   67.14{col 55}{space 3}0.000{col 63}{space 4} .7150509{col 76}{space 3} .7581242
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_scale_ i.wave##ib(2).Context if include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}     1,506
                                                {txt}F(3, 752)         =  {res}     5.69
                                                {txt}Prob > F          = {res}    0.0007
                                                {txt}R-squared         = {res}    0.0051
                                                {txt}Root MSE          =    {res} .25625

{txt}{ralign 87:(Std. err. adjusted for {res:753} clusters in {res:ID_random})}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}          Domi_scale_{col 23}{c |} Coefficient{col 35}  std. err.{col 47}      t{col 55}   P>|t|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}wave {c |}
{space 14}Wave 2  {c |}{col 23}{res}{space 2} .0477133{col 35}{space 2} .0118538{col 46}{space 1}    4.03{col 55}{space 3}0.000{col 63}{space 4} .0244428{col 76}{space 3} .0709837
{txt}{space 21} {c |}
{space 14}Context {c |}
{space 7}Conflict, now  {c |}{col 23}{res}{space 2} .0401593{col 35}{space 2} .0185499{col 46}{space 1}    2.16{col 55}{space 3}0.031{col 63}{space 4} .0037435{col 76}{space 3}  .076575
{txt}{space 21} {c |}
{space 9}wave#Context {c |}
Wave 2#Conflict, now  {c |}{col 23}{res}{space 2}-.0539521{col 35}{space 2} .0165531{col 46}{space 1}   -3.26{col 55}{space 3}0.001{col 63}{space 4}-.0864479{col 76}{space 3}-.0214564
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} .4872471{col 35}{space 2} .0127458{col 46}{space 1}   38.23{col 55}{space 3}0.000{col 63}{space 4} .4622256{col 76}{space 3} .5122687
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. *** SOM.7: Within-respondent test of the Conflict-Sensitivity Hypothesis using factor score variables for trait measurement
. ** Produces Table SOM.7.a.2 (testing within-respondent change among respondent assigned to peace condition in wave 1)
. eststo clear
{txt}
{com}. 
. eststo: reg Comp_PCA_ i.wave if include_PCA==1 & Context == 2, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       718
                                                {txt}F(1, 358)         =  {res}     8.01
                                                {txt}Prob > F          = {res}    0.0049
                                                {txt}R-squared         = {res}    0.0059
                                                {txt}Root MSE          =    {res} .88347

{txt}{ralign 78:(Std. err. adjusted for {res:359} clusters in {res:ID_random})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   Comp_PCA_{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}wave {c |}
{space 5}Wave 2  {c |}{col 14}{res}{space 2}-.1362304{col 26}{space 2} .0481478{col 37}{space 1}   -2.83{col 46}{space 3}0.005{col 54}{space 4}-.2309185{col 67}{space 3}-.0415423
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1545529{col 26}{space 2} .0419426{col 37}{space 1}    3.68{col 46}{space 3}0.000{col 54}{space 4} .0720679{col 67}{space 3} .2370378
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. eststo: reg Warm_PCA_ i.wave if include_PCA==1 & Context == 2, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       718
                                                {txt}F(1, 358)         =  {res}     1.10
                                                {txt}Prob > F          = {res}    0.2949
                                                {txt}R-squared         = {res}    0.0007
                                                {txt}Root MSE          =    {res}  .9559

{txt}{ralign 78:(Std. err. adjusted for {res:359} clusters in {res:ID_random})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   Warm_PCA_{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}wave {c |}
{space 5}Wave 2  {c |}{col 14}{res}{space 2} -.049396{col 26}{space 2} .0470866{col 37}{space 1}   -1.05{col 46}{space 3}0.295{col 54}{space 4} -.141997{col 67}{space 3}  .043205
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0740856{col 26}{space 2} .0504443{col 37}{space 1}    1.47{col 46}{space 3}0.143{col 54}{space 4}-.0251188{col 67}{space 3} .1732899
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. eststo: reg Domi_PCA_ i.wave if include_PCA==1 & Context == 2, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}       718
                                                {txt}F(1, 358)         =  {res}    11.29
                                                {txt}Prob > F          = {res}    0.0009
                                                {txt}R-squared         = {res}    0.0065
                                                {txt}Root MSE          =    {res} .94069

{txt}{ralign 78:(Std. err. adjusted for {res:359} clusters in {res:ID_random})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   Domi_PCA_{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}wave {c |}
{space 5}Wave 2  {c |}{col 14}{res}{space 2} .1515254{col 26}{space 2} .0451026{col 37}{space 1}    3.36{col 46}{space 3}0.001{col 54}{space 4} .0628261{col 67}{space 3} .2402248
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1184738{col 26}{space 2}  .049912{col 37}{space 1}   -2.37{col 46}{space 3}0.018{col 54}{space 4}-.2166314{col 67}{space 3}-.0203162
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. 
. esttab using TableSOM7a2.rtf, se(3) b(3) ar2 onecell label nobaselevels title("Table SOM.7.a.2: Within-respondent test of the Conflict-Sensitivity Hypothesis using factor scores as alternative measures of competence (Model 1), warmth (Model 2), and dominance (Model 3), with the sample restricted to responded assigned to the peace condition in Wave 1.") mtitle("Competence (PCA)" "Warmth (PCA)" "Dominance (PCA)") modelwidth() sfmt(0) replace compress star(* 0.05 ** 0.01 *** 0.001) nogaps
{res}{txt}{p 0 4 2}
(file {bf}
TableSOM7a2.rtf{rm}
not found)
{p_end}
(output written to {browse  `"TableSOM7a2.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. 
. * Full interactions between assigned experimental condition (assigned in wave 1) and wave (all respondents assigned to think of the ongoing war in wave 2)
. reg Comp_PCA_ i.wave##ib(2).Context if include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}     1,452
                                                {txt}F(3, 747)         =  {res}     3.13
                                                {txt}Prob > F          = {res}    0.0250
                                                {txt}R-squared         = {res}    0.0035
                                                {txt}Root MSE          =    {res} .90495

{txt}{ralign 87:(Std. err. adjusted for {res:748} clusters in {res:ID_random})}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}            Comp_PCA_{col 23}{c |} Coefficient{col 35}  std. err.{col 47}      t{col 55}   P>|t|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}wave {c |}
{space 14}Wave 2  {c |}{col 23}{res}{space 2}-.1318403{col 35}{space 2} .0478327{col 46}{space 1}   -2.76{col 55}{space 3}0.006{col 63}{space 4}-.2257428{col 76}{space 3}-.0379379
{txt}{space 21} {c |}
{space 14}Context {c |}
{space 7}Conflict, now  {c |}{col 23}{res}{space 2}-.0949738{col 35}{space 2} .0625124{col 46}{space 1}   -1.52{col 55}{space 3}0.129{col 63}{space 4}-.2176948{col 76}{space 3} .0277472
{txt}{space 21} {c |}
{space 9}wave#Context {c |}
Wave 2#Conflict, now  {c |}{col 23}{res}{space 2} .0984093{col 35}{space 2} .0722142{col 46}{space 1}    1.36{col 55}{space 3}0.173{col 63}{space 4}-.0433577{col 76}{space 3} .2401763
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} .1445638{col 35}{space 2} .0413564{col 46}{space 1}    3.50{col 55}{space 3}0.001{col 63}{space 4} .0633752{col 76}{space 3} .2257524
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Warm_PCA_ i.wave##ib(2).Context if include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}     1,452
                                                {txt}F(3, 747)         =  {res}     0.98
                                                {txt}Prob > F          = {res}    0.4006
                                                {txt}R-squared         = {res}    0.0020
                                                {txt}Root MSE          =    {res} .98865

{txt}{ralign 87:(Std. err. adjusted for {res:748} clusters in {res:ID_random})}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}            Warm_PCA_{col 23}{c |} Coefficient{col 35}  std. err.{col 47}      t{col 55}   P>|t|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}wave {c |}
{space 14}Wave 2  {c |}{col 23}{res}{space 2}-.0504754{col 35}{space 2} .0466751{col 46}{space 1}   -1.08{col 55}{space 3}0.280{col 63}{space 4}-.1421053{col 76}{space 3} .0411545
{txt}{space 21} {c |}
{space 14}Context {c |}
{space 7}Conflict, now  {c |}{col 23}{res}{space 2}-.1173588{col 35}{space 2} .0729627{col 46}{space 1}   -1.61{col 55}{space 3}0.108{col 63}{space 4}-.2605953{col 76}{space 3} .0258776
{txt}{space 21} {c |}
{space 9}wave#Context {c |}
Wave 2#Conflict, now  {c |}{col 23}{res}{space 2} .0751411{col 35}{space 2} .0694201{col 46}{space 1}    1.08{col 55}{space 3}0.279{col 63}{space 4}-.0611407{col 76}{space 3} .2114229
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} .0721408{col 35}{space 2} .0496147{col 46}{space 1}    1.45{col 55}{space 3}0.146{col 63}{space 4}-.0252599{col 76}{space 3} .1695416
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg Domi_PCA_ i.wave##ib(2).Context if include==1, cluster(ID_random)

{txt}Linear regression                               Number of obs     = {res}     1,452
                                                {txt}F(3, 747)         =  {res}     3.86
                                                {txt}Prob > F          = {res}    0.0094
                                                {txt}R-squared         = {res}    0.0039
                                                {txt}Root MSE          =    {res} .99433

{txt}{ralign 87:(Std. err. adjusted for {res:748} clusters in {res:ID_random})}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}            Domi_PCA_{col 23}{c |} Coefficient{col 35}  std. err.{col 47}      t{col 55}   P>|t|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}wave {c |}
{space 14}Wave 2  {c |}{col 23}{res}{space 2} .1238923{col 35}{space 2} .0457027{col 46}{space 1}    2.71{col 55}{space 3}0.007{col 63}{space 4} .0341712{col 76}{space 3} .2136133
{txt}{space 21} {c |}
{space 14}Context {c |}
{space 7}Conflict, now  {c |}{col 23}{res}{space 2} .1682441{col 35}{space 2} .0734209{col 46}{space 1}    2.29{col 55}{space 3}0.022{col 63}{space 4} .0241083{col 76}{space 3} .3123799
{txt}{space 21} {c |}
{space 9}wave#Context {c |}
Wave 2#Conflict, now  {c |}{col 23}{res}{space 2}-.2086936{col 35}{space 2}   .06582{col 46}{space 1}   -3.17{col 55}{space 3}0.002{col 63}{space 4}-.3379079{col 76}{space 3}-.0794794
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-.1018748{col 35}{space 2} .0493121{col 46}{space 1}   -2.07{col 55}{space 3}0.039{col 63}{space 4}-.1986816{col 76}{space 3} -.005068
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. *******************************************************************************************************************************************************
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\au206393\OneDrive - Aarhus universitet\Desktop\PSRM acceptance log-file\June 2025\Laustsen_et_al_PSRM_June2025.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}19 Jun 2025, 10:50:40
{txt}{.-}
{smcl}
{txt}{sf}{ul off}